NDM1_reproducibility.ipynb

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        "### 1. **Data compilation**\n",
        "\n",
        "*   1.1 Import original dataset and packages\n",
        "*   1.2 Import 12 sets of PADEL fingerprint outputs\n",
        "*   1.3 Define X and Y\n",
        "*   1.4 Feature selection\n",
        "\n",
        "### **2. Data splitting and modeling**\n",
        "\n",
        "\n",
        "*   2.1 Data splitting\n",
        "*   2.2 Random forest modeling and validation\n",
        "*   2.3 Hyperparameter tuning\n",
        "*   2.4 Modeling with tuned hyperparameters\n",
        "*   2.5 Y-randomization\n",
        "\n",
        "### **3. Feature importance**\n",
        "\n",
        "\n"
      ]
    },
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      "cell_type": "markdown",
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      "source": [
        "### **1. Data compilation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R50QMJoHjwZb"
      },
      "source": [
        "**1.1 Import original dataset and packages**"
      ]
    },
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      "source": [
        "# import dataset file\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/478concise.csv')\n",
        "df"
      ],
      "execution_count": 1,
      "outputs": [
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>pIC50</th>\n",
              "      <th>activity</th>\n",
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              "      <td>SC[C@H](C(=O)N1CCC[C@H]1C(=O)O)C</td>\n",
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              "      <td>SC[C@@H](C(=O)N(OC)C)C</td>\n",
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              "<p>478 rows × 4 columns</p>\n",
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              "                ID  ...                                   Canonical SMILES\n",
              "0    CHEMBL1327484  ...                         OC(=O)CSc1ccnc2c1ccc(c2)Cl\n",
              "1    CHEMBL1379384  ...                         CCCc1ccc(cc1)C(=O)CCC(=O)O\n",
              "2       CHEMBL1560  ...                   SC[C@H](C(=O)N1CCC[C@H]1C(=O)O)C\n",
              "3    CHEMBL3278535  ...                              N#Cc1coc2c(c1=O)cccc2\n",
              "4    CHEMBL3808541  ...           O=C(Nc1ccccc1C)CSc1nnc([nH]1)c1ccc(cc1)O\n",
              "..             ...  ...                                                ...\n",
              "473          lit95  ...                            CC(C(=O)Nc1ncc(s1)Br)CS\n",
              "474          lit96  ...                         OC(=O)CN(CC(=O)O)CCc1cccs1\n",
              "475          lit97  ...  C[C@H]([C@H]1C(=O)N2[C@@H]1CC(=C2C(=O)O)S[C@H]...\n",
              "476          lit98  ...                      OC(=O)CNC(=O)c1cccc(n1)C(=O)O\n",
              "477          lit99  ...                             SC[C@@H](C(=O)N(OC)C)C\n",
              "\n",
              "[478 rows x 4 columns]"
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          "execution_count": 1
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UoXmwXmDjlxy"
      },
      "source": [
        "**1.2 Import 12 sets of PADEL fingerprint outputs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fytxecXjhxbW"
      },
      "source": [
        "# A\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/AtomPairs2D_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iftKGPEBi3n2"
      },
      "source": [
        "# A\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/AtomPairs2DCount_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YvNFfI3jkW3"
      },
      "source": [
        "# C\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/CDK_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwwFL4A1kKdD"
      },
      "source": [
        "# C\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/CDKextended_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnFtXdSbk7SS"
      },
      "source": [
        "# C\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/CDKgraphonly_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7b7MGQ4leSF"
      },
      "source": [
        "# E\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/EState_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XWkztvHamALu"
      },
      "source": [
        "# M\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/MACCS_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pecHxRUyo1M-"
      },
      "source": [
        "# K\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/KlekotaRoth_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oBQylHhTY2ah"
      },
      "source": [
        "# K\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/KlekotaRothCount_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujRlq2KaV387"
      },
      "source": [
        "# P\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/PubChem_478.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zxTjRAUn5qW"
      },
      "source": [
        "# S\n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/Substructure_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "owh4ZLBSolh4"
      },
      "source": [
        "# S \n",
        "descriptors = pd.read_csv('https://raw.githubusercontent.com/GeorgeYuriCADD/IC50-dataset/main/SubstructureCount_478.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 488
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        "id": "l1GJzx46wU8u",
        "outputId": "8e6883de-dac2-4400-e4ca-a4fc0b16be4b"
      },
      "source": [
        "df3 = descriptors\n",
        "df3"
      ],
      "execution_count": 3,
      "outputs": [
        {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL1379384</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL1560</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL3278535</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL3808541</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>lit95</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>lit96</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>478 rows × 882 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45f198ed-45ee-4bfd-8cca-6b01ffe1643d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "              Name  PubchemFP0  ...  PubchemFP879  PubchemFP880\n",
              "0    CHEMBL1327484           1  ...             0             0\n",
              "1    CHEMBL1379384           1  ...             0             0\n",
              "2       CHEMBL1560           1  ...             0             0\n",
              "3    CHEMBL3278535           1  ...             0             0\n",
              "4    CHEMBL3808541           1  ...             0             0\n",
              "..             ...         ...  ...           ...           ...\n",
              "473          lit95           1  ...             0             0\n",
              "474          lit96           1  ...             0             0\n",
              "475          lit97           1  ...             0             0\n",
              "476          lit98           1  ...             0             0\n",
              "477          lit99           1  ...             0             0\n",
              "\n",
              "[478 rows x 882 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlnCHhw1kOUQ"
      },
      "source": [
        "**1.3 Define X and Y**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "al70oTeljG0S"
      },
      "source": [
        "X_raw = descriptors.drop('Name', axis=1)\n",
        "Y_raw = df['pIC50']"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNsuCThyki-E"
      },
      "source": [
        "**1.4 Feature selection**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "W2hQqFLajG3I",
        "outputId": "d4716d8b-9ab9-4e4b-92f2-9c4b282a071f"
      },
      "source": [
        "# removing low variance features\n",
        "\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "def remove_low_variance(input_data, threshold=0.1):\n",
        "    selection = VarianceThreshold(threshold)\n",
        "    selection.fit(input_data)\n",
        "    return input_data[input_data.columns[selection.get_support(indices=True)]]\n",
        "\n",
        "X_lowvariance = remove_low_variance(X_raw, threshold=0.1)\n",
        "X_lowvariance"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>PubchemFP2</th>\n",
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              "      <th>...</th>\n",
              "      <th>PubchemFP657</th>\n",
              "      <th>PubchemFP658</th>\n",
              "      <th>PubchemFP659</th>\n",
              "      <th>PubchemFP660</th>\n",
              "      <th>PubchemFP664</th>\n",
              "      <th>PubchemFP665</th>\n",
              "      <th>PubchemFP666</th>\n",
              "      <th>PubchemFP667</th>\n",
              "      <th>PubchemFP668</th>\n",
              "      <th>PubchemFP671</th>\n",
              "      <th>PubchemFP672</th>\n",
              "      <th>PubchemFP674</th>\n",
              "      <th>PubchemFP677</th>\n",
              "      <th>PubchemFP678</th>\n",
              "      <th>PubchemFP679</th>\n",
              "      <th>PubchemFP680</th>\n",
              "      <th>PubchemFP683</th>\n",
              "      <th>PubchemFP684</th>\n",
              "      <th>PubchemFP688</th>\n",
              "      <th>PubchemFP689</th>\n",
              "      <th>PubchemFP691</th>\n",
              "      <th>PubchemFP692</th>\n",
              "      <th>PubchemFP695</th>\n",
              "      <th>PubchemFP696</th>\n",
              "      <th>PubchemFP697</th>\n",
              "      <th>PubchemFP698</th>\n",
              "      <th>PubchemFP699</th>\n",
              "      <th>PubchemFP702</th>\n",
              "      <th>PubchemFP704</th>\n",
              "      <th>PubchemFP708</th>\n",
              "      <th>PubchemFP709</th>\n",
              "      <th>PubchemFP710</th>\n",
              "      <th>PubchemFP712</th>\n",
              "      <th>PubchemFP714</th>\n",
              "      <th>PubchemFP758</th>\n",
              "      <th>PubchemFP776</th>\n",
              "      <th>PubchemFP777</th>\n",
              "      <th>PubchemFP797</th>\n",
              "      <th>PubchemFP819</th>\n",
              "      <th>PubchemFP821</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>478 rows × 242 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20e10586-4bc3-471d-99a9-9ca400e3673b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-20e10586-4bc3-471d-99a9-9ca400e3673b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-20e10586-4bc3-471d-99a9-9ca400e3673b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     PubchemFP2  PubchemFP12  ...  PubchemFP819  PubchemFP821\n",
              "0             0            0  ...             0             1\n",
              "1             1            0  ...             0             0\n",
              "2             0            0  ...             0             0\n",
              "3             0            0  ...             1             0\n",
              "4             1            1  ...             0             1\n",
              "..          ...          ...  ...           ...           ...\n",
              "473           0            0  ...             0             0\n",
              "474           0            0  ...             0             0\n",
              "475           1            0  ...             0             0\n",
              "476           0            0  ...             0             0\n",
              "477           0            0  ...             0             0\n",
              "\n",
              "[478 rows x 242 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPpVTdHFlEZg"
      },
      "source": [
        "### **2. Data splitting and modeling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndrHE-Gkl6re"
      },
      "source": [
        "**2.1 Data splitting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBwyZNVjwU0p"
      },
      "source": [
        "# split dataset into training set and test set by 80/20 ratio\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X_lowvariance, Y_raw, test_size=0.2,random_state=42)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISgDrGVCwUxW",
        "outputId": "de0e3a06-e509-4de6-f6a0-6e692db6de23"
      },
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((382, 242), (96, 242))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5sshQo_Ukka",
        "outputId": "5072ac7d-5de7-487c-b914-d17d6b868d41"
      },
      "source": [
        "Y_train.shape, Y_test.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((382,), (96,))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "_tezBRVkGRBc",
        "outputId": "f40e4788-cfa9-4eb7-e68d-d7ca09338f11"
      },
      "source": [
        "X_test"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>PubchemFP2</th>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 242 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c43055d-ccb3-4502-b838-28bfdc26d5c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6c43055d-ccb3-4502-b838-28bfdc26d5c4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6c43055d-ccb3-4502-b838-28bfdc26d5c4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     PubchemFP2  PubchemFP12  ...  PubchemFP819  PubchemFP821\n",
              "469           0            0  ...             0             0\n",
              "33            0            0  ...             0             0\n",
              "131           0            1  ...             0             0\n",
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              "78            0            0  ...             0             0\n",
              "..          ...          ...  ...           ...           ...\n",
              "140           1            1  ...             0             0\n",
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              "\n",
              "[96 rows x 242 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tR64Yifcl9P0"
      },
      "source": [
        "**2.2 Random forest modeling and validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxRGa3BIjG_f"
      },
      "source": [
        "# import Regressor \n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbGnc9UqjHCV",
        "outputId": "602d491e-38fc-4718-c859-0de123ad7ae0"
      },
      "source": [
        "rf0 = RandomForestRegressor(n_estimators=500, max_features=3, random_state=42)\n",
        "rf0.fit(X_train,Y_train)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_features=3, n_estimators=500, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeas8W8IjHEu"
      },
      "source": [
        "# establish prediction\n",
        "\n",
        "Y_train_pred = rf0.predict(X_train)\n",
        "Y_test_pred = rf0.predict(X_test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "acgcpQh7jHT0",
        "outputId": "48a9b071-8939-4851-f6d6-8fce6a2ef620"
      },
      "source": [
        "# model performance\n",
        "\n",
        "import pickle\n",
        "import numpy as np \n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_squared_error \n",
        "from pandas import DataFrame,Series\n",
        "from sklearn.model_selection import train_test_split,cross_val_score, cross_val_predict\n",
        "import missingno as msno \n",
        "import seaborn as sns \n",
        "\n",
        "# Calculating model performance\n",
        "r2_train = r2_score(Y_train, Y_train_pred)\n",
        "r2_test = r2_score(Y_test, Y_test_pred)\n",
        "\n",
        "rmse_train = mean_squared_error(Y_train, Y_train_pred,squared= False)\n",
        "rmse_test = mean_squared_error(Y_test, Y_test_pred,squared= False)\n",
        "\n",
        "\n",
        "# Preparing performance summary table\n",
        "model_name = pd.Series([rf0], name='Algorithm')\n",
        "\n",
        "r2_train_series = pd.Series(r2_train, name='r2_train')\n",
        "r2_test_series = pd.Series(r2_test, name='r2_test')\n",
        "\n",
        "rmse_train_series = pd.Series(rmse_train, name='rmse_train')\n",
        "rmse_test_series = pd.Series(rmse_test, name='rmse_test')\n",
        "\n",
        "performance_metrics = pd.concat([model_name,\n",
        "                                   r2_train_series, r2_test_series, \n",
        "                                   rmse_train_series, rmse_test_series, \n",
        "                                  ], axis=1)\n",
        "\n",
        "performance_metrics['r2_train-test'] = abs(performance_metrics['r2_train'] - performance_metrics['r2_test'])\n",
        "\n",
        "performance_metrics"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>r2_train</th>\n",
              "      <th>r2_test</th>\n",
              "      <th>rmse_train</th>\n",
              "      <th>rmse_test</th>\n",
              "      <th>r2_train-test</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>(DecisionTreeRegressor(max_features=3, random_...</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-e4711586-2442-4ccb-bc96-e17f04e428ed');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "                                           Algorithm  ...  r2_train-test\n",
              "0  (DecisionTreeRegressor(max_features=3, random_...  ...        0.32273\n",
              "\n",
              "[1 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jdZiJSbjrD_4"
      },
      "source": [
        "**2.3 Hyperparameter tuning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5OxKR0qJb-e"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "import numpy as np\n",
        "\n",
        "max_features_range = np.arange(1,6,1)\n",
        "n_estimators_range = np.arange(0,600,15)\n",
        "param_grid = dict(max_features=max_features_range, n_estimators=n_estimators_range)\n",
        "\n",
        "grid=GridSearchCV(rf0,param_grid,scoring='r2',cv=3,verbose=3)\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiQ26qfjJdnZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa088103-c56c-4e6f-b814-88789bbf7bc1"
      },
      "source": [
        "# fit the grid\n",
        "grid.fit(X_train, Y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 200 candidates, totalling 600 fits\n",
            "[CV 1/3] END ......max_features=1, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 2/3] END ......max_features=1, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 3/3] END ......max_features=1, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 1/3] END ...max_features=1, n_estimators=15;, score=0.439 total time=   0.0s\n",
            "[CV 2/3] END ...max_features=1, n_estimators=15;, score=0.474 total time=   0.0s\n",
            "[CV 3/3] END ...max_features=1, n_estimators=15;, score=0.250 total time=   0.0s\n",
            "[CV 1/3] END ...max_features=1, n_estimators=30;, score=0.490 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=1, n_estimators=30;, score=0.407 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=1, n_estimators=30;, score=0.312 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=1, n_estimators=45;, score=0.479 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=1, n_estimators=45;, score=0.433 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=1, n_estimators=45;, score=0.325 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=1, n_estimators=60;, score=0.491 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=1, n_estimators=60;, score=0.444 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=1, n_estimators=60;, score=0.343 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=1, n_estimators=75;, score=0.486 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=1, n_estimators=75;, score=0.450 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=1, n_estimators=75;, score=0.336 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=1, n_estimators=90;, score=0.475 total time=   0.2s\n",
            "[CV 2/3] END ...max_features=1, n_estimators=90;, score=0.454 total time=   0.2s\n",
            "[CV 3/3] END ...max_features=1, n_estimators=90;, score=0.341 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=105;, score=0.477 total time=   0.2s\n",
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            "[CV 1/3] END ..max_features=1, n_estimators=120;, score=0.477 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=120;, score=0.443 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=120;, score=0.343 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=135;, score=0.472 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=135;, score=0.442 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=135;, score=0.351 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=150;, score=0.470 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=150;, score=0.450 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=150;, score=0.355 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=165;, score=0.468 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=165;, score=0.455 total time=   0.3s\n",
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            "[CV 3/3] END ..max_features=1, n_estimators=180;, score=0.354 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=195;, score=0.473 total time=   0.3s\n",
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            "[CV 1/3] END ..max_features=1, n_estimators=210;, score=0.471 total time=   0.4s\n",
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            "[CV 3/3] END ..max_features=1, n_estimators=210;, score=0.359 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=225;, score=0.469 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=225;, score=0.444 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=225;, score=0.358 total time=   0.4s\n",
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            "[CV 2/3] END ..max_features=1, n_estimators=240;, score=0.447 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=240;, score=0.356 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=255;, score=0.465 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=255;, score=0.443 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=255;, score=0.351 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=270;, score=0.464 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=270;, score=0.443 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=270;, score=0.352 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=285;, score=0.462 total time=   0.5s\n",
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            "[CV 3/3] END ..max_features=1, n_estimators=285;, score=0.347 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=300;, score=0.466 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=300;, score=0.442 total time=   0.5s\n",
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            "[CV 2/3] END ..max_features=1, n_estimators=315;, score=0.436 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=315;, score=0.346 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=330;, score=0.466 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=330;, score=0.439 total time=   0.5s\n",
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            "[CV 2/3] END ..max_features=1, n_estimators=345;, score=0.440 total time=   0.6s\n",
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            "[CV 1/3] END ..max_features=1, n_estimators=360;, score=0.469 total time=   0.6s\n",
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            "[CV 3/3] END ..max_features=1, n_estimators=360;, score=0.348 total time=   0.6s\n",
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            "[CV 2/3] END ..max_features=1, n_estimators=375;, score=0.445 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=375;, score=0.349 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=390;, score=0.468 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=390;, score=0.443 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=390;, score=0.342 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=405;, score=0.466 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=405;, score=0.443 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=405;, score=0.342 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=420;, score=0.466 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=420;, score=0.443 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=420;, score=0.344 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=435;, score=0.466 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=435;, score=0.443 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=435;, score=0.347 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=450;, score=0.467 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=450;, score=0.443 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=450;, score=0.348 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=465;, score=0.467 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=465;, score=0.443 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=465;, score=0.347 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=480;, score=0.468 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=480;, score=0.441 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=480;, score=0.344 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=495;, score=0.469 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=495;, score=0.443 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=495;, score=0.343 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=510;, score=0.469 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=510;, score=0.443 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=510;, score=0.343 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=525;, score=0.470 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=525;, score=0.441 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=525;, score=0.343 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=540;, score=0.470 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=540;, score=0.442 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=540;, score=0.341 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=555;, score=0.472 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=555;, score=0.442 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=555;, score=0.343 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=570;, score=0.470 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=570;, score=0.443 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=570;, score=0.342 total time=   1.0s\n",
            "[CV 1/3] END ..max_features=1, n_estimators=585;, score=0.468 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=1, n_estimators=585;, score=0.442 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=1, n_estimators=585;, score=0.342 total time=   1.0s\n",
            "[CV 1/3] END ......max_features=2, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 2/3] END ......max_features=2, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 3/3] END ......max_features=2, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 1/3] END ...max_features=2, n_estimators=15;, score=0.404 total time=   0.0s\n",
            "[CV 2/3] END ...max_features=2, n_estimators=15;, score=0.369 total time=   0.0s\n",
            "[CV 3/3] END ...max_features=2, n_estimators=15;, score=0.317 total time=   0.0s\n",
            "[CV 1/3] END ...max_features=2, n_estimators=30;, score=0.486 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=2, n_estimators=30;, score=0.398 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=2, n_estimators=30;, score=0.395 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=2, n_estimators=45;, score=0.475 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=2, n_estimators=45;, score=0.413 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=2, n_estimators=45;, score=0.391 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=2, n_estimators=60;, score=0.483 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=2, n_estimators=60;, score=0.423 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=2, n_estimators=60;, score=0.368 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=2, n_estimators=75;, score=0.494 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=2, n_estimators=75;, score=0.437 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=2, n_estimators=75;, score=0.350 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=2, n_estimators=90;, score=0.495 total time=   0.2s\n",
            "[CV 2/3] END ...max_features=2, n_estimators=90;, score=0.440 total time=   0.2s\n",
            "[CV 3/3] END ...max_features=2, n_estimators=90;, score=0.347 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=105;, score=0.485 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=105;, score=0.448 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=105;, score=0.368 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=120;, score=0.490 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=120;, score=0.452 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=120;, score=0.364 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=135;, score=0.479 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=135;, score=0.450 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=135;, score=0.368 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=150;, score=0.483 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=150;, score=0.453 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=150;, score=0.365 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=165;, score=0.487 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=165;, score=0.457 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=165;, score=0.368 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=180;, score=0.483 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=180;, score=0.461 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=180;, score=0.368 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=195;, score=0.483 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=195;, score=0.462 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=195;, score=0.364 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=210;, score=0.481 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=210;, score=0.464 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=210;, score=0.359 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=225;, score=0.484 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=225;, score=0.466 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=225;, score=0.358 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=240;, score=0.485 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=240;, score=0.462 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=240;, score=0.358 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=255;, score=0.484 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=255;, score=0.461 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=255;, score=0.360 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=270;, score=0.482 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=270;, score=0.458 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=270;, score=0.361 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=285;, score=0.482 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=285;, score=0.458 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=285;, score=0.364 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=300;, score=0.481 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=300;, score=0.460 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=300;, score=0.364 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=315;, score=0.481 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=315;, score=0.458 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=315;, score=0.362 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=330;, score=0.481 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=330;, score=0.459 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=330;, score=0.362 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=345;, score=0.482 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=345;, score=0.459 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=345;, score=0.360 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=360;, score=0.483 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=360;, score=0.461 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=360;, score=0.358 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=375;, score=0.484 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=375;, score=0.461 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=375;, score=0.358 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=390;, score=0.483 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=390;, score=0.460 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=390;, score=0.357 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=405;, score=0.481 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=405;, score=0.460 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=405;, score=0.355 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=420;, score=0.479 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=420;, score=0.459 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=420;, score=0.354 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=435;, score=0.480 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=435;, score=0.458 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=435;, score=0.355 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=450;, score=0.481 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=450;, score=0.458 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=450;, score=0.353 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=465;, score=0.480 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=465;, score=0.456 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=465;, score=0.352 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=480;, score=0.480 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=480;, score=0.456 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=480;, score=0.352 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=495;, score=0.480 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=495;, score=0.455 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=495;, score=0.350 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=510;, score=0.481 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=510;, score=0.453 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=510;, score=0.350 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=525;, score=0.479 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=525;, score=0.454 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=525;, score=0.348 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=540;, score=0.480 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=540;, score=0.455 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=540;, score=0.348 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=555;, score=0.479 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=555;, score=0.456 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=555;, score=0.348 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=570;, score=0.479 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=570;, score=0.458 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=570;, score=0.348 total time=   1.0s\n",
            "[CV 1/3] END ..max_features=2, n_estimators=585;, score=0.478 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=2, n_estimators=585;, score=0.457 total time=   1.0s\n",
            "[CV 3/3] END ..max_features=2, n_estimators=585;, score=0.348 total time=   1.0s\n",
            "[CV 1/3] END ......max_features=3, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 2/3] END ......max_features=3, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 3/3] END ......max_features=3, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 1/3] END ...max_features=3, n_estimators=15;, score=0.468 total time=   0.0s\n",
            "[CV 2/3] END ...max_features=3, n_estimators=15;, score=0.458 total time=   0.0s\n",
            "[CV 3/3] END ...max_features=3, n_estimators=15;, score=0.304 total time=   0.0s\n",
            "[CV 1/3] END ...max_features=3, n_estimators=30;, score=0.495 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=3, n_estimators=30;, score=0.450 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=3, n_estimators=30;, score=0.359 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=3, n_estimators=45;, score=0.494 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=3, n_estimators=45;, score=0.457 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=3, n_estimators=45;, score=0.353 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=3, n_estimators=60;, score=0.494 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=3, n_estimators=60;, score=0.448 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=3, n_estimators=60;, score=0.372 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=3, n_estimators=75;, score=0.507 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=3, n_estimators=75;, score=0.439 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=3, n_estimators=75;, score=0.363 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=3, n_estimators=90;, score=0.503 total time=   0.2s\n",
            "[CV 2/3] END ...max_features=3, n_estimators=90;, score=0.443 total time=   0.2s\n",
            "[CV 3/3] END ...max_features=3, n_estimators=90;, score=0.353 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=105;, score=0.499 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=105;, score=0.445 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=105;, score=0.360 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=120;, score=0.498 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=120;, score=0.444 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=120;, score=0.358 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=135;, score=0.497 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=135;, score=0.451 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=135;, score=0.345 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=150;, score=0.497 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=150;, score=0.452 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=150;, score=0.333 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=165;, score=0.492 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=165;, score=0.454 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=165;, score=0.330 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=180;, score=0.492 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=180;, score=0.457 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=180;, score=0.332 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=195;, score=0.496 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=195;, score=0.457 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=195;, score=0.333 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=210;, score=0.497 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=210;, score=0.451 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=210;, score=0.336 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=225;, score=0.497 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=225;, score=0.455 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=225;, score=0.338 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=240;, score=0.499 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=240;, score=0.452 total time=   0.4s\n",
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            "[CV 1/3] END ..max_features=3, n_estimators=255;, score=0.495 total time=   0.4s\n",
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            "[CV 1/3] END ..max_features=3, n_estimators=270;, score=0.495 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=270;, score=0.455 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=270;, score=0.339 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=285;, score=0.493 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=285;, score=0.454 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=285;, score=0.338 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=300;, score=0.494 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=300;, score=0.452 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=300;, score=0.339 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=315;, score=0.493 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=315;, score=0.454 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=315;, score=0.342 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=330;, score=0.490 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=330;, score=0.450 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=330;, score=0.345 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=345;, score=0.494 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=345;, score=0.448 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=345;, score=0.343 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=360;, score=0.495 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=360;, score=0.449 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=360;, score=0.343 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=375;, score=0.495 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=375;, score=0.449 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=375;, score=0.344 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=390;, score=0.495 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=390;, score=0.448 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=390;, score=0.344 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=405;, score=0.493 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=405;, score=0.447 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=405;, score=0.344 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=420;, score=0.491 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=420;, score=0.446 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=420;, score=0.343 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=435;, score=0.490 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=435;, score=0.446 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=435;, score=0.343 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=450;, score=0.490 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=450;, score=0.447 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=450;, score=0.344 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=465;, score=0.490 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=465;, score=0.449 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=465;, score=0.342 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=480;, score=0.491 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=480;, score=0.448 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=480;, score=0.341 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=495;, score=0.489 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=495;, score=0.449 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=495;, score=0.341 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=510;, score=0.489 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=510;, score=0.449 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=510;, score=0.340 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=525;, score=0.489 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=525;, score=0.449 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=525;, score=0.340 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=540;, score=0.487 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=540;, score=0.448 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=540;, score=0.342 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=555;, score=0.488 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=555;, score=0.448 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=555;, score=0.346 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=570;, score=0.487 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=570;, score=0.449 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=570;, score=0.345 total time=   1.0s\n",
            "[CV 1/3] END ..max_features=3, n_estimators=585;, score=0.485 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=3, n_estimators=585;, score=0.448 total time=   1.0s\n",
            "[CV 3/3] END ..max_features=3, n_estimators=585;, score=0.344 total time=   1.0s\n",
            "[CV 1/3] END ......max_features=4, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 2/3] END ......max_features=4, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 3/3] END ......max_features=4, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 1/3] END ...max_features=4, n_estimators=15;, score=0.452 total time=   0.0s\n",
            "[CV 2/3] END ...max_features=4, n_estimators=15;, score=0.473 total time=   0.0s\n",
            "[CV 3/3] END ...max_features=4, n_estimators=15;, score=0.232 total time=   0.0s\n",
            "[CV 1/3] END ...max_features=4, n_estimators=30;, score=0.460 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=4, n_estimators=30;, score=0.447 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=4, n_estimators=30;, score=0.274 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=4, n_estimators=45;, score=0.463 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=4, n_estimators=45;, score=0.432 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=4, n_estimators=45;, score=0.282 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=4, n_estimators=60;, score=0.471 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=4, n_estimators=60;, score=0.444 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=4, n_estimators=60;, score=0.314 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=4, n_estimators=75;, score=0.485 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=4, n_estimators=75;, score=0.449 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=4, n_estimators=75;, score=0.322 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=4, n_estimators=90;, score=0.484 total time=   0.2s\n",
            "[CV 2/3] END ...max_features=4, n_estimators=90;, score=0.446 total time=   0.2s\n",
            "[CV 3/3] END ...max_features=4, n_estimators=90;, score=0.319 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=105;, score=0.481 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=105;, score=0.445 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=105;, score=0.336 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=120;, score=0.485 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=120;, score=0.442 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=120;, score=0.335 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=135;, score=0.489 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=135;, score=0.439 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=135;, score=0.336 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=150;, score=0.487 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=150;, score=0.442 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=150;, score=0.340 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=165;, score=0.484 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=165;, score=0.442 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=165;, score=0.340 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=180;, score=0.487 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=180;, score=0.443 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=180;, score=0.338 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=195;, score=0.490 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=195;, score=0.441 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=195;, score=0.344 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=210;, score=0.493 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=210;, score=0.439 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=210;, score=0.345 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=225;, score=0.489 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=225;, score=0.440 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=225;, score=0.345 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=240;, score=0.490 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=240;, score=0.444 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=240;, score=0.348 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=255;, score=0.487 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=255;, score=0.444 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=255;, score=0.349 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=270;, score=0.487 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=270;, score=0.447 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=270;, score=0.345 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=285;, score=0.489 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=285;, score=0.450 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=285;, score=0.345 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=300;, score=0.489 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=300;, score=0.449 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=300;, score=0.342 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=315;, score=0.488 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=315;, score=0.449 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=315;, score=0.345 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=330;, score=0.487 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=330;, score=0.451 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=330;, score=0.348 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=345;, score=0.486 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=345;, score=0.452 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=345;, score=0.346 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=360;, score=0.487 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=360;, score=0.453 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=360;, score=0.346 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=375;, score=0.490 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=375;, score=0.452 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=375;, score=0.346 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=390;, score=0.490 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=390;, score=0.452 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=390;, score=0.345 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=405;, score=0.487 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=405;, score=0.455 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=405;, score=0.346 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=420;, score=0.486 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=420;, score=0.457 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=420;, score=0.346 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=435;, score=0.485 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=435;, score=0.455 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=435;, score=0.345 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=450;, score=0.486 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=450;, score=0.455 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=450;, score=0.346 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=465;, score=0.486 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=465;, score=0.455 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=465;, score=0.346 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=480;, score=0.486 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=480;, score=0.454 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=480;, score=0.346 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=495;, score=0.486 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=495;, score=0.454 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=495;, score=0.346 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=510;, score=0.486 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=510;, score=0.453 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=510;, score=0.347 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=525;, score=0.483 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=525;, score=0.451 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=525;, score=0.346 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=540;, score=0.483 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=540;, score=0.451 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=540;, score=0.346 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=555;, score=0.484 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=555;, score=0.452 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=555;, score=0.347 total time=   1.0s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=570;, score=0.482 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=570;, score=0.452 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=570;, score=0.348 total time=   1.0s\n",
            "[CV 1/3] END ..max_features=4, n_estimators=585;, score=0.482 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=4, n_estimators=585;, score=0.450 total time=   1.0s\n",
            "[CV 3/3] END ..max_features=4, n_estimators=585;, score=0.346 total time=   1.0s\n",
            "[CV 1/3] END ......max_features=5, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 2/3] END ......max_features=5, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 3/3] END ......max_features=5, n_estimators=0;, score=nan total time=   0.0s\n",
            "[CV 1/3] END ...max_features=5, n_estimators=15;, score=0.458 total time=   0.0s\n",
            "[CV 2/3] END ...max_features=5, n_estimators=15;, score=0.461 total time=   0.0s\n",
            "[CV 3/3] END ...max_features=5, n_estimators=15;, score=0.341 total time=   0.0s\n",
            "[CV 1/3] END ...max_features=5, n_estimators=30;, score=0.483 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=5, n_estimators=30;, score=0.443 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=5, n_estimators=30;, score=0.341 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=5, n_estimators=45;, score=0.480 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=5, n_estimators=45;, score=0.445 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=5, n_estimators=45;, score=0.356 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=5, n_estimators=60;, score=0.498 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=5, n_estimators=60;, score=0.449 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=5, n_estimators=60;, score=0.357 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=5, n_estimators=75;, score=0.511 total time=   0.1s\n",
            "[CV 2/3] END ...max_features=5, n_estimators=75;, score=0.457 total time=   0.1s\n",
            "[CV 3/3] END ...max_features=5, n_estimators=75;, score=0.357 total time=   0.1s\n",
            "[CV 1/3] END ...max_features=5, n_estimators=90;, score=0.510 total time=   0.2s\n",
            "[CV 2/3] END ...max_features=5, n_estimators=90;, score=0.458 total time=   0.2s\n",
            "[CV 3/3] END ...max_features=5, n_estimators=90;, score=0.357 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=105;, score=0.512 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=105;, score=0.449 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=105;, score=0.366 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=120;, score=0.509 total time=   0.2s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=120;, score=0.443 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=120;, score=0.358 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=135;, score=0.510 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=135;, score=0.448 total time=   0.2s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=135;, score=0.355 total time=   0.2s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=150;, score=0.512 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=150;, score=0.455 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=150;, score=0.352 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=165;, score=0.506 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=165;, score=0.461 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=165;, score=0.346 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=180;, score=0.502 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=180;, score=0.459 total time=   0.3s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=180;, score=0.347 total time=   0.3s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=195;, score=0.500 total time=   0.3s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=195;, score=0.457 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=195;, score=0.351 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=210;, score=0.502 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=210;, score=0.452 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=210;, score=0.353 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=225;, score=0.502 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=225;, score=0.455 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=225;, score=0.358 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=240;, score=0.501 total time=   0.4s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=240;, score=0.455 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=240;, score=0.360 total time=   0.4s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=255;, score=0.496 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=255;, score=0.453 total time=   0.4s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=255;, score=0.355 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=270;, score=0.494 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=270;, score=0.452 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=270;, score=0.353 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=285;, score=0.494 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=285;, score=0.452 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=285;, score=0.354 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=300;, score=0.493 total time=   0.5s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=300;, score=0.451 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=300;, score=0.354 total time=   0.5s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=315;, score=0.492 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=315;, score=0.449 total time=   0.5s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=315;, score=0.355 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=330;, score=0.490 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=330;, score=0.448 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=330;, score=0.356 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=345;, score=0.488 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=345;, score=0.447 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=345;, score=0.356 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=360;, score=0.490 total time=   0.6s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=360;, score=0.446 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=360;, score=0.354 total time=   0.6s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=375;, score=0.492 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=375;, score=0.445 total time=   0.6s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=375;, score=0.356 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=390;, score=0.493 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=390;, score=0.444 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=390;, score=0.355 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=405;, score=0.492 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=405;, score=0.446 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=405;, score=0.355 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=420;, score=0.492 total time=   0.7s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=420;, score=0.446 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=420;, score=0.355 total time=   0.7s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=435;, score=0.491 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=435;, score=0.445 total time=   0.7s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=435;, score=0.355 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=450;, score=0.491 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=450;, score=0.448 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=450;, score=0.358 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=465;, score=0.494 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=465;, score=0.448 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=465;, score=0.359 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=480;, score=0.492 total time=   0.8s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=480;, score=0.449 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=480;, score=0.359 total time=   0.8s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=495;, score=0.492 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=495;, score=0.451 total time=   0.8s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=495;, score=0.358 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=510;, score=0.491 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=510;, score=0.451 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=510;, score=0.356 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=525;, score=0.492 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=525;, score=0.453 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=525;, score=0.357 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=540;, score=0.491 total time=   0.9s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=540;, score=0.452 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=540;, score=0.356 total time=   0.9s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=555;, score=0.491 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=555;, score=0.453 total time=   0.9s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=555;, score=0.357 total time=   1.0s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=570;, score=0.491 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=570;, score=0.453 total time=   1.0s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=570;, score=0.358 total time=   1.0s\n",
            "[CV 1/3] END ..max_features=5, n_estimators=585;, score=0.491 total time=   1.0s\n",
            "[CV 2/3] END ..max_features=5, n_estimators=585;, score=0.454 total time=   1.0s\n",
            "[CV 3/3] END ..max_features=5, n_estimators=585;, score=0.358 total time=   1.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "15 fits failed out of a total of 600.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "15 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 681, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_forest.py\", line 384, in fit\n",
            "    self._validate_estimator()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_base.py\", line 140, in _validate_estimator\n",
            "    self.n_estimators\n",
            "ValueError: n_estimators must be greater than zero, got 0.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [       nan 0.38755839 0.40275836 0.41257407 0.42596494 0.42392593\n",
            " 0.42326659 0.42076894 0.42113242 0.42161367 0.42493346 0.42607413\n",
            " 0.42523647 0.4279086  0.42434553 0.42358986 0.4242956  0.41972456\n",
            " 0.41948076 0.41734658 0.41836454 0.4156711  0.41735179 0.41786899\n",
            " 0.41981402 0.42040027 0.41755719 0.41694481 0.41784618 0.41868377\n",
            " 0.41945654 0.41881962 0.41767631 0.41840302 0.41836558 0.41788171\n",
            " 0.41785046 0.4186846  0.41815337 0.41714494        nan 0.36331747\n",
            " 0.4262447  0.42633727 0.42446648 0.42699045 0.42767215 0.43358395\n",
            " 0.43549797 0.43223558 0.43356626 0.43707413 0.43742206 0.43604366\n",
            " 0.43466999 0.4358644  0.43492022 0.43478924 0.43345557 0.43441896\n",
            " 0.43456799 0.43351262 0.43379839 0.43359113 0.43401639 0.43445916\n",
            " 0.43346433 0.43195425 0.43084505 0.43093547 0.43078379 0.42928276\n",
            " 0.42941871 0.42868798 0.42787609 0.42697239 0.42752977 0.4275664\n",
            " 0.42793852 0.42743646        nan 0.41025895 0.43470231 0.43457369\n",
            " 0.43757242 0.43670157 0.43324522 0.43458627 0.43339252 0.4310287\n",
            " 0.4272737  0.4251463  0.42688304 0.42834946 0.42816916 0.42995615\n",
            " 0.42968797 0.42965196 0.4295846  0.42868513 0.42845774 0.42979442\n",
            " 0.42820366 0.42822876 0.42897082 0.42927737 0.42909595 0.42794117\n",
            " 0.42697839 0.42669929 0.42694648 0.4270265  0.42662857 0.42626949\n",
            " 0.42610233 0.42565344 0.42572476 0.42727304 0.42706302 0.42556141\n",
            "        nan 0.38571715 0.39364059 0.39195565 0.40952086 0.41885135\n",
            " 0.4165073  0.42091952 0.42071571 0.42160779 0.42286986 0.42191766\n",
            " 0.42252463 0.4249084  0.42573145 0.424881   0.4271569  0.42649373\n",
            " 0.42636929 0.42779424 0.42655055 0.42743945 0.42860023 0.42817457\n",
            " 0.4287494  0.42924109 0.42887079 0.42925089 0.42959613 0.42876502\n",
            " 0.42919193 0.42875107 0.42885121 0.42864008 0.42849349 0.4266547\n",
            " 0.42701185 0.42738479 0.4273861  0.42579632        nan 0.42015793\n",
            " 0.42235307 0.42726719 0.43471552 0.44139536 0.44136385 0.44250479\n",
            " 0.43676105 0.43750797 0.43982843 0.43758053 0.43591007 0.43592293\n",
            " 0.43600977 0.43799583 0.43883417 0.43463434 0.43308203 0.43325056\n",
            " 0.43274198 0.4319815  0.43127331 0.43050152 0.42986416 0.43099397\n",
            " 0.43088941 0.43091786 0.4308913  0.43047041 0.43216253 0.4337678\n",
            " 0.43334333 0.43371186 0.43248512 0.4340216  0.43292995 0.43339838\n",
            " 0.43414548 0.43429258]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3,\n",
              "             estimator=RandomForestRegressor(max_features=3, n_estimators=500,\n",
              "                                             random_state=42),\n",
              "             param_grid={'max_features': array([1, 2, 3, 4, 5]),\n",
              "                         'n_estimators': array([  0,  15,  30,  45,  60,  75,  90, 105, 120, 135, 150, 165, 180,\n",
              "       195, 210, 225, 240, 255, 270, 285, 300, 315, 330, 345, 360, 375,\n",
              "       390, 405, 420, 435, 450, 465, 480, 495, 510, 525, 540, 555, 570,\n",
              "       585])},\n",
              "             scoring='r2', verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_Yg1wWLJfG9",
        "outputId": "544fd293-4842-4e4e-a988-6f9c4bc703d5"
      },
      "source": [
        "print(\"The best parameters are %s with a score of %0.2f\"\n",
        "      % (grid.best_params_, grid.best_score_))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best parameters are {'max_features': 5, 'n_estimators': 105} with a score of 0.44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GxmteAZ1Jhjv",
        "outputId": "9db0b407-8306-4c3b-b723-3b72d70c8a85"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "grid_results = pd.concat([pd.DataFrame(grid.cv_results_[\"params\"]),pd.DataFrame(grid.cv_results_[\"mean_test_score\"], columns=[\"score\"])],axis=1)\n",
        "grid_results"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-272c127f-a3ee-44de-afe1-9378ce757116\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>max_features</th>\n",
              "      <th>n_estimators</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>0.387558</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>0.402758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>0.412574</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>60</td>\n",
              "      <td>0.425965</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>5</td>\n",
              "      <td>525</td>\n",
              "      <td>0.434022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>5</td>\n",
              "      <td>540</td>\n",
              "      <td>0.432930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>5</td>\n",
              "      <td>555</td>\n",
              "      <td>0.433398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>5</td>\n",
              "      <td>570</td>\n",
              "      <td>0.434145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>5</td>\n",
              "      <td>585</td>\n",
              "      <td>0.434293</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-272c127f-a3ee-44de-afe1-9378ce757116')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-272c127f-a3ee-44de-afe1-9378ce757116 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-272c127f-a3ee-44de-afe1-9378ce757116');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     max_features  n_estimators     score\n",
              "0               1             0       NaN\n",
              "1               1            15  0.387558\n",
              "2               1            30  0.402758\n",
              "3               1            45  0.412574\n",
              "4               1            60  0.425965\n",
              "..            ...           ...       ...\n",
              "195             5           525  0.434022\n",
              "196             5           540  0.432930\n",
              "197             5           555  0.433398\n",
              "198             5           570  0.434145\n",
              "199             5           585  0.434293\n",
              "\n",
              "[200 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "BgSSG4_Oc6MV",
        "outputId": "437d57e4-93ed-479a-cc27-e1297ed04c1e"
      },
      "source": [
        "grid_contour = grid_results.groupby(['max_features','n_estimators']).mean()\n",
        "grid_contour"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-625d3e79-cb4a-41f6-a2b2-e5cbbe8e2bc5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
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              "    <tr>\n",
              "      <th>max_features</th>\n",
              "      <th>n_estimators</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
              "      <th>0</th>\n",
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              "      <th>15</th>\n",
              "      <td>0.387558</td>\n",
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              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0.402758</td>\n",
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              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>0.412574</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.425965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
              "      <th>525</th>\n",
              "      <td>0.434022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540</th>\n",
              "      <td>0.432930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555</th>\n",
              "      <td>0.433398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570</th>\n",
              "      <td>0.434145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>585</th>\n",
              "      <td>0.434293</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-625d3e79-cb4a-41f6-a2b2-e5cbbe8e2bc5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-625d3e79-cb4a-41f6-a2b2-e5cbbe8e2bc5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-625d3e79-cb4a-41f6-a2b2-e5cbbe8e2bc5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                              score\n",
              "max_features n_estimators          \n",
              "1            0                  NaN\n",
              "             15            0.387558\n",
              "             30            0.402758\n",
              "             45            0.412574\n",
              "             60            0.425965\n",
              "...                             ...\n",
              "5            525           0.434022\n",
              "             540           0.432930\n",
              "             555           0.433398\n",
              "             570           0.434145\n",
              "             585           0.434293\n",
              "\n",
              "[200 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "nPOG9XlGc78Q",
        "outputId": "b790cf95-3163-41b2-d2ff-a47c406023ec"
      },
      "source": [
        "grid_reset = grid_contour.reset_index()\n",
        "grid_reset.columns = ['max_features', 'n_estimators', 'r2']\n",
        "grid_pivot = grid_reset.pivot('max_features', 'n_estimators')\n",
        "grid_pivot"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bb59512f-2530-4796-b183-73f3683df8ec\">\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"40\" halign=\"left\">r2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n_estimators</th>\n",
              "      <th>0</th>\n",
              "      <th>15</th>\n",
              "      <th>30</th>\n",
              "      <th>45</th>\n",
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              "      <th>75</th>\n",
              "      <th>90</th>\n",
              "      <th>105</th>\n",
              "      <th>120</th>\n",
              "      <th>135</th>\n",
              "      <th>150</th>\n",
              "      <th>165</th>\n",
              "      <th>180</th>\n",
              "      <th>195</th>\n",
              "      <th>210</th>\n",
              "      <th>225</th>\n",
              "      <th>240</th>\n",
              "      <th>255</th>\n",
              "      <th>270</th>\n",
              "      <th>285</th>\n",
              "      <th>300</th>\n",
              "      <th>315</th>\n",
              "      <th>330</th>\n",
              "      <th>345</th>\n",
              "      <th>360</th>\n",
              "      <th>375</th>\n",
              "      <th>390</th>\n",
              "      <th>405</th>\n",
              "      <th>420</th>\n",
              "      <th>435</th>\n",
              "      <th>450</th>\n",
              "      <th>465</th>\n",
              "      <th>480</th>\n",
              "      <th>495</th>\n",
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              "      <th>525</th>\n",
              "      <th>540</th>\n",
              "      <th>555</th>\n",
              "      <th>570</th>\n",
              "      <th>585</th>\n",
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              "    <tr>\n",
              "      <th>max_features</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.387558</td>\n",
              "      <td>0.402758</td>\n",
              "      <td>0.412574</td>\n",
              "      <td>0.425965</td>\n",
              "      <td>0.423926</td>\n",
              "      <td>0.423267</td>\n",
              "      <td>0.420769</td>\n",
              "      <td>0.421132</td>\n",
              "      <td>0.421614</td>\n",
              "      <td>0.424933</td>\n",
              "      <td>0.426074</td>\n",
              "      <td>0.425236</td>\n",
              "      <td>0.427909</td>\n",
              "      <td>0.424346</td>\n",
              "      <td>0.423590</td>\n",
              "      <td>0.424296</td>\n",
              "      <td>0.419725</td>\n",
              "      <td>0.419481</td>\n",
              "      <td>0.417347</td>\n",
              "      <td>0.418365</td>\n",
              "      <td>0.415671</td>\n",
              "      <td>0.417352</td>\n",
              "      <td>0.417869</td>\n",
              "      <td>0.419814</td>\n",
              "      <td>0.420400</td>\n",
              "      <td>0.417557</td>\n",
              "      <td>0.416945</td>\n",
              "      <td>0.417846</td>\n",
              "      <td>0.418684</td>\n",
              "      <td>0.419457</td>\n",
              "      <td>0.418820</td>\n",
              "      <td>0.417676</td>\n",
              "      <td>0.418403</td>\n",
              "      <td>0.418366</td>\n",
              "      <td>0.417882</td>\n",
              "      <td>0.417850</td>\n",
              "      <td>0.418685</td>\n",
              "      <td>0.418153</td>\n",
              "      <td>0.417145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.363317</td>\n",
              "      <td>0.426245</td>\n",
              "      <td>0.426337</td>\n",
              "      <td>0.424466</td>\n",
              "      <td>0.426990</td>\n",
              "      <td>0.427672</td>\n",
              "      <td>0.433584</td>\n",
              "      <td>0.435498</td>\n",
              "      <td>0.432236</td>\n",
              "      <td>0.433566</td>\n",
              "      <td>0.437074</td>\n",
              "      <td>0.437422</td>\n",
              "      <td>0.436044</td>\n",
              "      <td>0.434670</td>\n",
              "      <td>0.435864</td>\n",
              "      <td>0.434920</td>\n",
              "      <td>0.434789</td>\n",
              "      <td>0.433456</td>\n",
              "      <td>0.434419</td>\n",
              "      <td>0.434568</td>\n",
              "      <td>0.433513</td>\n",
              "      <td>0.433798</td>\n",
              "      <td>0.433591</td>\n",
              "      <td>0.434016</td>\n",
              "      <td>0.434459</td>\n",
              "      <td>0.433464</td>\n",
              "      <td>0.431954</td>\n",
              "      <td>0.430845</td>\n",
              "      <td>0.430935</td>\n",
              "      <td>0.430784</td>\n",
              "      <td>0.429283</td>\n",
              "      <td>0.429419</td>\n",
              "      <td>0.428688</td>\n",
              "      <td>0.427876</td>\n",
              "      <td>0.426972</td>\n",
              "      <td>0.427530</td>\n",
              "      <td>0.427566</td>\n",
              "      <td>0.427939</td>\n",
              "      <td>0.427436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.410259</td>\n",
              "      <td>0.434702</td>\n",
              "      <td>0.434574</td>\n",
              "      <td>0.437572</td>\n",
              "      <td>0.436702</td>\n",
              "      <td>0.433245</td>\n",
              "      <td>0.434586</td>\n",
              "      <td>0.433393</td>\n",
              "      <td>0.431029</td>\n",
              "      <td>0.427274</td>\n",
              "      <td>0.425146</td>\n",
              "      <td>0.426883</td>\n",
              "      <td>0.428349</td>\n",
              "      <td>0.428169</td>\n",
              "      <td>0.429956</td>\n",
              "      <td>0.429688</td>\n",
              "      <td>0.429652</td>\n",
              "      <td>0.429585</td>\n",
              "      <td>0.428685</td>\n",
              "      <td>0.428458</td>\n",
              "      <td>0.429794</td>\n",
              "      <td>0.428204</td>\n",
              "      <td>0.428229</td>\n",
              "      <td>0.428971</td>\n",
              "      <td>0.429277</td>\n",
              "      <td>0.429096</td>\n",
              "      <td>0.427941</td>\n",
              "      <td>0.426978</td>\n",
              "      <td>0.426699</td>\n",
              "      <td>0.426946</td>\n",
              "      <td>0.427027</td>\n",
              "      <td>0.426629</td>\n",
              "      <td>0.426269</td>\n",
              "      <td>0.426102</td>\n",
              "      <td>0.425653</td>\n",
              "      <td>0.425725</td>\n",
              "      <td>0.427273</td>\n",
              "      <td>0.427063</td>\n",
              "      <td>0.425561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.385717</td>\n",
              "      <td>0.393641</td>\n",
              "      <td>0.391956</td>\n",
              "      <td>0.409521</td>\n",
              "      <td>0.418851</td>\n",
              "      <td>0.416507</td>\n",
              "      <td>0.420920</td>\n",
              "      <td>0.420716</td>\n",
              "      <td>0.421608</td>\n",
              "      <td>0.422870</td>\n",
              "      <td>0.421918</td>\n",
              "      <td>0.422525</td>\n",
              "      <td>0.424908</td>\n",
              "      <td>0.425731</td>\n",
              "      <td>0.424881</td>\n",
              "      <td>0.427157</td>\n",
              "      <td>0.426494</td>\n",
              "      <td>0.426369</td>\n",
              "      <td>0.427794</td>\n",
              "      <td>0.426551</td>\n",
              "      <td>0.427439</td>\n",
              "      <td>0.428600</td>\n",
              "      <td>0.428175</td>\n",
              "      <td>0.428749</td>\n",
              "      <td>0.429241</td>\n",
              "      <td>0.428871</td>\n",
              "      <td>0.429251</td>\n",
              "      <td>0.429596</td>\n",
              "      <td>0.428765</td>\n",
              "      <td>0.429192</td>\n",
              "      <td>0.428751</td>\n",
              "      <td>0.428851</td>\n",
              "      <td>0.428640</td>\n",
              "      <td>0.428493</td>\n",
              "      <td>0.426655</td>\n",
              "      <td>0.427012</td>\n",
              "      <td>0.427385</td>\n",
              "      <td>0.427386</td>\n",
              "      <td>0.425796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.420158</td>\n",
              "      <td>0.422353</td>\n",
              "      <td>0.427267</td>\n",
              "      <td>0.434716</td>\n",
              "      <td>0.441395</td>\n",
              "      <td>0.441364</td>\n",
              "      <td>0.442505</td>\n",
              "      <td>0.436761</td>\n",
              "      <td>0.437508</td>\n",
              "      <td>0.439828</td>\n",
              "      <td>0.437581</td>\n",
              "      <td>0.435910</td>\n",
              "      <td>0.435923</td>\n",
              "      <td>0.436010</td>\n",
              "      <td>0.437996</td>\n",
              "      <td>0.438834</td>\n",
              "      <td>0.434634</td>\n",
              "      <td>0.433082</td>\n",
              "      <td>0.433251</td>\n",
              "      <td>0.432742</td>\n",
              "      <td>0.431981</td>\n",
              "      <td>0.431273</td>\n",
              "      <td>0.430502</td>\n",
              "      <td>0.429864</td>\n",
              "      <td>0.430994</td>\n",
              "      <td>0.430889</td>\n",
              "      <td>0.430918</td>\n",
              "      <td>0.430891</td>\n",
              "      <td>0.430470</td>\n",
              "      <td>0.432163</td>\n",
              "      <td>0.433768</td>\n",
              "      <td>0.433343</td>\n",
              "      <td>0.433712</td>\n",
              "      <td>0.432485</td>\n",
              "      <td>0.434022</td>\n",
              "      <td>0.432930</td>\n",
              "      <td>0.433398</td>\n",
              "      <td>0.434145</td>\n",
              "      <td>0.434293</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb59512f-2530-4796-b183-73f3683df8ec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bb59512f-2530-4796-b183-73f3683df8ec button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bb59512f-2530-4796-b183-73f3683df8ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              r2                      ...                              \n",
              "n_estimators 0         15        30   ...       555       570       585\n",
              "max_features                          ...                              \n",
              "1            NaN  0.387558  0.402758  ...  0.418685  0.418153  0.417145\n",
              "2            NaN  0.363317  0.426245  ...  0.427566  0.427939  0.427436\n",
              "3            NaN  0.410259  0.434702  ...  0.427273  0.427063  0.425561\n",
              "4            NaN  0.385717  0.393641  ...  0.427385  0.427386  0.425796\n",
              "5            NaN  0.420158  0.422353  ...  0.433398  0.434145  0.434293\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQjOM1NtdBj4"
      },
      "source": [
        "x = grid_pivot.columns.levels[1].values\n",
        "y = grid_pivot.index.values\n",
        "z = grid_pivot.values"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "7gJjXf_krwty",
        "outputId": "5c610bcd-d602-477f-c1da-c9f922e28a15"
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "# X and Y axes labels\n",
        "layout = go.Layout(\n",
        "            xaxis=go.layout.XAxis(\n",
        "              title=go.layout.xaxis.Title(\n",
        "              text='n_estimators')\n",
        "             ),\n",
        "             yaxis=go.layout.YAxis(\n",
        "              title=go.layout.yaxis.Title(\n",
        "              text='max_features') \n",
        "            ) )\n",
        "\n",
        "fig = go.Figure(data = [go.Contour(z=z, x=x, y=y)], layout=layout )\n",
        "\n",
        "fig.update_layout(title='NDM-1 hyperparameter tuning', autosize=False,\n",
        "                  width=800, height=800,\n",
        "                  margin=dict(l=65, r=50, b=65, t=90))\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFxTRaQdrq66"
      },
      "source": [
        "**2.4 Modeling with tuned hyperparameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPOW6HHUjHZS",
        "outputId": "642ea878-4106-4219-9574-db72ba694ce3"
      },
      "source": [
        "rf1 = RandomForestRegressor(n_estimators = 105, max_features= 5, random_state= 42)\n",
        "rf1.fit(X_train, Y_train)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(max_features=5, n_estimators=105, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "swc455m7jHcH"
      },
      "source": [
        "Y_train_pred1 = rf1.predict(X_train)\n",
        "Y_test_pred1 = rf1.predict(X_test)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "id": "PdLtXMtWsI7s",
        "outputId": "024c583f-2434-4485-fb08-37a8dd803326"
      },
      "source": [
        "# scatter plot to visualize and evaluate model\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.set(color_codes=True)\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "ax = sns.regplot(Y_train, Y_train_pred1, scatter_kws={'alpha':0.4})\n",
        "ax.set_xlabel('Training_set pIC50', fontsize='large', fontweight='bold')\n",
        "ax.set_ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
        "ax.set_xlim(2, 9)\n",
        "ax.set_ylim(2, 9)\n",
        "ax.figure.set_size_inches(10, 10)\n",
        "plt.show"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 723
        },
        "id": "mYzreB2TsVBX",
        "outputId": "f93fdc9f-523d-4607-ebaf-64ee74688e57"
      },
      "source": [
        "sns.set(color_codes=True)\n",
        "sns.set_style(\"white\")\n",
        "\n",
        "ax = sns.regplot(Y_test, Y_test_pred1, scatter_kws={'alpha':0.4})\n",
        "ax.set_xlabel('Testing_set pIC50', fontsize='large', fontweight='bold')\n",
        "ax.set_ylabel('Predicted pIC50', fontsize='large', fontweight='bold')\n",
        "ax.set_xlim(2, 9)\n",
        "ax.set_ylim(2, 9)\n",
        "ax.figure.set_size_inches(10, 10)\n",
        "plt.show"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPSQKeIpsbjO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "16c26efc-2af7-4ca4-940f-f16ce5f0f876"
      },
      "source": [
        "# model performance\n",
        "\n",
        "r2_train = r2_score(Y_train, Y_train_pred1)\n",
        "r2_test = r2_score(Y_test, Y_test_pred1)\n",
        "\n",
        "rmse_train = mean_squared_error(Y_train, Y_train_pred1,squared= False)\n",
        "rmse_test = mean_squared_error(Y_test, Y_test_pred1,squared= False)\n",
        "\n",
        "\n",
        "# Preparing performance summary table\n",
        "model_name = pd.Series([rf1], name='Algorithm')\n",
        "\n",
        "r2_train_series = pd.Series(r2_train, name='r2_train')\n",
        "r2_test_series = pd.Series(r2_test, name='r2_test')\n",
        "\n",
        "rmse_train_series = pd.Series(rmse_train, name='rmse_train')\n",
        "rmse_test_series = pd.Series(rmse_test, name='rmse_test')\n",
        "\n",
        "performance_metrics = pd.concat([model_name,\n",
        "                                   r2_train_series, r2_test_series, \n",
        "                                   rmse_train_series, rmse_test_series, \n",
        "                                  ], axis=1)\n",
        "\n",
        "performance_metrics['r2_train-test'] = abs(performance_metrics['r2_train'] - performance_metrics['r2_test'])\n",
        "\n",
        "performance_metrics"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Algorithm</th>\n",
              "      <th>r2_train</th>\n",
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              "      <th>rmse_test</th>\n",
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              "                                           Algorithm  ...  r2_train-test\n",
              "0  (DecisionTreeRegressor(max_features=5, random_...  ...       0.307821\n",
              "\n",
              "[1 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ss3V_XFWtDKl",
        "outputId": "28f14f8d-e57e-420c-8fb6-383119a3fc6a"
      },
      "source": [
        "# 10-fold cross-validation\n",
        "from numpy import mean\n",
        "from numpy import std\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# prepare the cross-validation procedure\n",
        "cv = KFold(n_splits=5, random_state=42, shuffle=True)\n",
        "\n",
        "# evaluate model\n",
        "scores = cross_val_score(rf1, X_train, Y_train, cv=10, scoring='r2')\n",
        "\n",
        "# report performance\n",
        "print('r2: %.3f (%.3f)' % (mean(scores), std(scores)))\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2: 0.497 (0.179)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cTfnETWtQXe",
        "outputId": "13b71254-c328-4ba7-96db-058624c13d6c"
      },
      "source": [
        "# 10-fold cross-validation\n",
        "\n",
        "cv = KFold(n_splits=10, random_state=42, shuffle=True)\n",
        "\n",
        "# evaluate model\n",
        "scores = cross_val_score(rf1, X_train, Y_train, cv=10, scoring='neg_mean_squared_error')\n",
        "\n",
        "# report performance\n",
        "print('neg_mean_squared_error: %.3f (%.3f)' % (mean(scores), std(scores)))\n",
        "mse_scores = -scores\n",
        "rmse_scores = np.sqrt(mse_scores)\n",
        "print(rmse_scores)\n",
        "print(rmse_scores.mean())"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "neg_mean_squared_error: -0.619 (0.201)\n",
            "[0.72281639 1.0010756  0.65893762 0.5392754  0.83398906 0.72581917\n",
            " 0.81176874 0.69963742 0.82690229 0.94053709]\n",
            "0.7760758792029765\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jugOwrsGthJ7"
      },
      "source": [
        "**2.5 Y-ramdomization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMjT2aFDtnV4",
        "outputId": "932e4036-9a64-494a-de87-f7afade82f43"
      },
      "source": [
        "# Training on original target\n",
        "\n",
        "import numpy as np\n",
        "from sklearn.metrics import r2_score\n",
        "\n",
        "rf1 = RandomForestRegressor(n_estimators = 105, max_features= 5, random_state= 42)\n",
        "rf1.fit(X_train, Y_train)\n",
        "\n",
        "Y_train_pred1 = rf1.predict(X_train)\n",
        "original_r2 = r2_score(Y_train,Y_train_pred1)\n",
        "print(original_r2)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9309028639114101\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ka_dcm8tn29",
        "outputId": "8f9ace6a-026b-42ef-f506-625195c363ea"
      },
      "source": [
        "# Training on shuffuled target\n",
        "\n",
        "shuffled_r2 = []\n",
        "  \n",
        "for i in range(100):\n",
        "    np.random.shuffle(Y_raw)\n",
        "\n",
        "    rf2 = RandomForestRegressor().fit(X_lowvariance,Y_raw)\n",
        "      \n",
        "    Y_train_pred2 = rf2.predict(X_train)\n",
        "    Y_test_pred2 = rf2.predict(X_test)\n",
        "    shuffled_r2.append(r2_score(Y_test,Y_test_pred2))\n",
        "  \n",
        "print(shuffled_r2[:20])"
      ],
      "execution_count": 31,
      "outputs": [
        {
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          "name": "stderr",
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
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            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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            "\n",
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            "\n",
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            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.5377094043177209, -0.5275939244799241, -0.3363051165791895, -0.11652859755787714, -0.0982662873544562, -0.15270567750568897, -0.44490926356049143, -0.33637077618696987, -0.30345646142849625, -0.1675040171714075, -0.34886025746609994, -0.5766477418192981, -0.2530296570132695, -0.5265362707308938, -0.47431852038851563, -0.44472117870342287, -0.3749982185982237, -0.49230896865465623, -0.442572317346293, -0.2517640388610247]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "kZ2J9gjkuXYg",
        "outputId": "617f2a66-3a5b-4f2f-8e01-71248c0e1fa4"
      },
      "source": [
        "# Histogram plot\n",
        "\n",
        "sns.set_style(\"white\")\n",
        "plt.figure(figsize = (8, 8)) \n",
        "\n",
        "ax = sns.histplot(data=shuffled_r2, color=\"skyblue\", kde=True, bins = 10)\n",
        "plt.axvline(original_r2)\n",
        "ax.set(xlim=(-1, 1))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(-1.0, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cGR3QAs8ufBD"
      },
      "source": [
        "### **3. Feature importance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdUsy3-a8ZX1"
      },
      "source": [
        "df4 = pd.DataFrame(rf1.feature_names_in_)\n",
        "df5= pd.DataFrame(rf1.feature_importances_)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-G19d1m-E7hY",
        "outputId": "524fd0d0-1ee9-4387-bc3b-a85c763fec6c"
      },
      "source": [
        "df_importance = pd.concat([df4,df5],axis =1)\n",
        "df_importance"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b502f844-be8e-47e7-9e9c-b318e88a9560');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                0         0\n",
              "0      PubchemFP2  0.008560\n",
              "1     PubchemFP12  0.009880\n",
              "2     PubchemFP14  0.001837\n",
              "3     PubchemFP15  0.006927\n",
              "4     PubchemFP16  0.003842\n",
              "..            ...       ...\n",
              "237  PubchemFP776  0.007019\n",
              "238  PubchemFP777  0.003784\n",
              "239  PubchemFP797  0.004151\n",
              "240  PubchemFP819  0.004834\n",
              "241  PubchemFP821  0.003987\n",
              "\n",
              "[242 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 708
        },
        "id": "KN48IfpFFTLf",
        "outputId": "8ff0beae-d685-4099-b2cc-e70627f66048"
      },
      "source": [
        "df_importance.columns = ['Feature','importance']\n",
        "df6 = df_importance.sort_values(by='importance',ascending=False).head(21)\n",
        "df6"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Feature</th>\n",
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              "      <th>81</th>\n",
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              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>PubchemFP696</td>\n",
              "      <td>0.008156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>PubchemFP20</td>\n",
              "      <td>0.008090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>PubchemFP353</td>\n",
              "      <td>0.008022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>PubchemFP643</td>\n",
              "      <td>0.007943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>PubchemFP449</td>\n",
              "      <td>0.007738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>PubchemFP585</td>\n",
              "      <td>0.007693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PubchemFP293</td>\n",
              "      <td>0.007618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>PubchemFP528</td>\n",
              "      <td>0.007617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>PubchemFP633</td>\n",
              "      <td>0.007616</td>\n",
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              "</table>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5dd2aeac-9d9b-4749-a7f9-344fd5153f24');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          Feature  importance\n",
              "29   PubchemFP338    0.011696\n",
              "47   PubchemFP374    0.011507\n",
              "123  PubchemFP523    0.010649\n",
              "64   PubchemFP392    0.010325\n",
              "1     PubchemFP12    0.009880\n",
              "7     PubchemFP33    0.009863\n",
              "26   PubchemFP308    0.009036\n",
              "186  PubchemFP628    0.008844\n",
              "15   PubchemFP185    0.008779\n",
              "91   PubchemFP451    0.008665\n",
              "0      PubchemFP2    0.008560\n",
              "81   PubchemFP437    0.008244\n",
              "225  PubchemFP696    0.008156\n",
              "6     PubchemFP20    0.008090\n",
              "36   PubchemFP353    0.008022\n",
              "194  PubchemFP643    0.007943\n",
              "89   PubchemFP449    0.007738\n",
              "160  PubchemFP585    0.007693\n",
              "23   PubchemFP293    0.007618\n",
              "126  PubchemFP528    0.007617\n",
              "187  PubchemFP633    0.007616"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "bkpTTL3pFS-_",
        "outputId": "35506edf-3c65-4fc8-f806-632b4e5190ac"
      },
      "source": [
        "df6.sort_values(by='importance',ascending = True).plot(kind='barh',x='Feature',y='importance',figsize=(12,10),fontsize=18)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff9d8b0cbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}