Classifier: Comparing_Classifications_Models.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Comparing Classifications Models.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMs/bDrCqQB+8mciw/oNkIi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/ML-Algos/blob/main/Comparing_Classifications_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8aVNDO2KWDl"
      },
      "source": [
        "#Generate Synthetic data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oog9A34SJMKg"
      },
      "source": [
        "#Import Library\n",
        "import pandas as pd\n",
        "from sklearn.datasets import make_classification"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-aqacks1KQMq"
      },
      "source": [
        "#Generate dataset\n",
        "X, y = make_classification(n_samples=1000, n_classes=2, n_features=5, n_redundant=0, random_state=1)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCtYBYGsKv5l",
        "outputId": "7ce95f63-308e-468d-81b0-774a9de55617",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X.shape, y.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1000, 5), (1000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQJGemrvKyVi"
      },
      "source": [
        "#Split dataset\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6_vn2OBK60F"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjVmdfOmK_wE",
        "outputId": "e97dfed9-aebe-497c-a296-99bda8b8713d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800, 5), (800,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nibg5dUyLEe-",
        "outputId": "a5f864db-c1f4-4e21-f145-86c78d12faef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_test.shape, y_test.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((200, 5), (200,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z28Cnl5fLcBH"
      },
      "source": [
        "#Build Classification Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8P9EhAKLLJ0I"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.gaussian_process.kernels import RBF\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "from sklearn.linear_model import SGDClassifier"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_VHFkc2MPvm"
      },
      "source": [
        "##Defining learning classifiers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6mody7SMRAv"
      },
      "source": [
        "names = [\"Nearest_Neighbors\", \"Linear_SVM\", \"Polynomial_SVM\", \"RBF_SVM\", \"Gaussian_Process\",\n",
        "         \"Gradient_Boosting\", \"Decision_Tree\", \"Extra_Trees\", \"Random_Forest\", \"Neural_Net\", \"AdaBoost\",\n",
        "         \"Naive_Bayes\", \"QDA\", \"SGD\"]\n",
        "\n",
        "classifiers = [\n",
        "    KNeighborsClassifier(3),\n",
        "    SVC(kernel=\"linear\", C=0.025),\n",
        "    SVC(kernel=\"poly\", degree=3, C=0.025),\n",
        "    SVC(kernel=\"rbf\", C=1, gamma=2),\n",
        "    GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
        "    GradientBoostingClassifier(n_estimators=100, learning_rate=1.0),\n",
        "    DecisionTreeClassifier(max_depth=5),\n",
        "    ExtraTreesClassifier(n_estimators=10, min_samples_split=2),\n",
        "    RandomForestClassifier(max_depth=5, n_estimators=100),\n",
        "    MLPClassifier(alpha=1, max_iter=1000),\n",
        "    AdaBoostClassifier(n_estimators=100),\n",
        "    GaussianNB(),\n",
        "    QuadraticDiscriminantAnalysis(),\n",
        "    SGDClassifier(loss=\"hinge\", penalty=\"l2\")]"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rw9ZaMQhMZDx"
      },
      "source": [
        "##Build Model, Apply Model on Test Data & Record Accuracy Scores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfG5yvwwMVso"
      },
      "source": [
        "scores = []\n",
        "for name, clf in zip(names, classifiers):\n",
        "    clf.fit(X_train, y_train)\n",
        "    score = clf.score(X_test, y_test)\n",
        "    scores.append(score)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u0qb2Ap-Mg3N"
      },
      "source": [
        "#Model Performance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94F1zIoXMrFw"
      },
      "source": [
        "##Create data frame of model performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pebcXqFdMc2S",
        "outputId": "76ad2990-90fb-4ac1-a227-ccb026afa564",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "df = pd.DataFrame()\n",
        "df['name'] = names\n",
        "df['score'] = scores\n",
        "df"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\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>name</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nearest_Neighbors</td>\n",
              "      <td>0.815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Linear_SVM</td>\n",
              "      <td>0.840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Polynomial_SVM</td>\n",
              "      <td>0.800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RBF_SVM</td>\n",
              "      <td>0.830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gaussian_Process</td>\n",
              "      <td>0.875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Gradient_Boosting</td>\n",
              "      <td>0.835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Decision_Tree</td>\n",
              "      <td>0.845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Extra_Trees</td>\n",
              "      <td>0.825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Random_Forest</td>\n",
              "      <td>0.860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Neural_Net</td>\n",
              "      <td>0.870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>0.835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Naive_Bayes</td>\n",
              "      <td>0.855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>QDA</td>\n",
              "      <td>0.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>SGD</td>\n",
              "      <td>0.830</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 name  score\n",
              "0   Nearest_Neighbors  0.815\n",
              "1          Linear_SVM  0.840\n",
              "2      Polynomial_SVM  0.800\n",
              "3             RBF_SVM  0.830\n",
              "4    Gaussian_Process  0.875\n",
              "5   Gradient_Boosting  0.835\n",
              "6       Decision_Tree  0.845\n",
              "7         Extra_Trees  0.825\n",
              "8       Random_Forest  0.860\n",
              "9          Neural_Net  0.870\n",
              "10           AdaBoost  0.835\n",
              "11        Naive_Bayes  0.855\n",
              "12                QDA  0.850\n",
              "13                SGD  0.830"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WimP0PFoNZ-v"
      },
      "source": [
        "##Adding colors to the data frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-29UzKSMulR",
        "outputId": "b5f2f7a2-45e0-4e06-8d69-018ba435a368",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "#https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html\n",
        "import seaborn as sns\n",
        "cm = sns.light_palette(\"green\", as_cmap=True)\n",
        "s = df.style.background_gradient(cmap=cm)\n",
        "s"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              "#T_99460e36_0be3_11eb_a8ee_0242ac1c0002row0_col1{\n",
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              "            background-color:  #6db66d;\n",
              "            color:  #000000;\n",
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              "            background-color:  #ebf3eb;\n",
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              "        }#T_99460e36_0be3_11eb_a8ee_0242ac1c0002row3_col1,#T_99460e36_0be3_11eb_a8ee_0242ac1c0002row13_col1{\n",
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              "        }#T_99460e36_0be3_11eb_a8ee_0242ac1c0002row4_col1{\n",
              "            background-color:  #008000;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_99460e36_0be3_11eb_a8ee_0242ac1c0002row5_col1,#T_99460e36_0be3_11eb_a8ee_0242ac1c0002row10_col1{\n",
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              "            background-color:  #4ea64e;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >name</th>        <th class=\"col_heading level0 col1\" >score</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row0_col0\" class=\"data row0 col0\" >Nearest_Neighbors</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.815000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row1_col0\" class=\"data row1 col0\" >Linear_SVM</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.840000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row2_col0\" class=\"data row2 col0\" >Polynomial_SVM</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.800000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row3_col0\" class=\"data row3 col0\" >RBF_SVM</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.830000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row4_col0\" class=\"data row4 col0\" >Gaussian_Process</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.875000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row5_col0\" class=\"data row5 col0\" >Gradient_Boosting</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.835000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row6_col0\" class=\"data row6 col0\" >Decision_Tree</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.845000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row7_col0\" class=\"data row7 col0\" >Extra_Trees</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.825000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row8_col0\" class=\"data row8 col0\" >Random_Forest</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.860000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row9_col0\" class=\"data row9 col0\" >Neural_Net</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.870000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row10_col0\" class=\"data row10 col0\" >AdaBoost</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.835000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row11_col0\" class=\"data row11 col0\" >Naive_Bayes</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.855000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row12_col0\" class=\"data row12 col0\" >QDA</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row12_col1\" class=\"data row12 col1\" >0.850000</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row13_col0\" class=\"data row13 col0\" >SGD</td>\n",
              "                        <td id=\"T_99460e36_0be3_11eb_a8ee_0242ac1c0002row13_col1\" class=\"data row13 col1\" >0.830000</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7fe2bce8b630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k9v0c-6cNhch"
      },
      "source": [
        "##Bar plot of model performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGmtNegaNebN",
        "outputId": "c88992df-5ffd-4b3d-ff47-658852560bd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=\"name\", x=\"score\", data=df)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}