3-1_K_Nearest_Neighbor.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzh-M3AdBaGG"
      },
      "source": [
        "# k-최근접 이웃 회귀"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POjvkKCdBaGJ"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/3-1.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5J2cFzCrDWT"
      },
      "source": [
        "## 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fL3wuWxD0cH6"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "np5j0UTtJNI_"
      },
      "outputs": [],
      "source": [
        "perch_length = np.array(\n",
        "    [8.4, 13.7, 15.0, 16.2, 17.4, 18.0, 18.7, 19.0, 19.6, 20.0, \n",
        "     21.0, 21.0, 21.0, 21.3, 22.0, 22.0, 22.0, 22.0, 22.0, 22.5, \n",
        "     22.5, 22.7, 23.0, 23.5, 24.0, 24.0, 24.6, 25.0, 25.6, 26.5, \n",
        "     27.3, 27.5, 27.5, 27.5, 28.0, 28.7, 30.0, 32.8, 34.5, 35.0, \n",
        "     36.5, 36.0, 37.0, 37.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0, \n",
        "     40.0, 42.0, 43.0, 43.0, 43.5, 44.0]\n",
        "     )\n",
        "perch_weight = np.array(\n",
        "    [5.9, 32.0, 40.0, 51.5, 70.0, 100.0, 78.0, 80.0, 85.0, 85.0, \n",
        "     110.0, 115.0, 125.0, 130.0, 120.0, 120.0, 130.0, 135.0, 110.0, \n",
        "     130.0, 150.0, 145.0, 150.0, 170.0, 225.0, 145.0, 188.0, 180.0, \n",
        "     197.0, 218.0, 300.0, 260.0, 265.0, 250.0, 250.0, 300.0, 320.0, \n",
        "     514.0, 556.0, 840.0, 685.0, 700.0, 700.0, 690.0, 900.0, 650.0, \n",
        "     820.0, 850.0, 900.0, 1015.0, 820.0, 1100.0, 1000.0, 1100.0, \n",
        "     1000.0, 1000.0]\n",
        "     )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "perch_length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9dqoNj9_jY9",
        "outputId": "7c675c94-3826-4e01-c060-7ca2214647a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8.4, 13.7, 15. , 16.2, 17.4, 18. , 18.7, 19. , 19.6, 20. , 21. ,\n",
              "       21. , 21. , 21.3, 22. , 22. , 22. , 22. , 22. , 22.5, 22.5, 22.7,\n",
              "       23. , 23.5, 24. , 24. , 24.6, 25. , 25.6, 26.5, 27.3, 27.5, 27.5,\n",
              "       27.5, 28. , 28.7, 30. , 32.8, 34.5, 35. , 36.5, 36. , 37. , 37. ,\n",
              "       39. , 39. , 39. , 40. , 40. , 40. , 40. , 42. , 43. , 43. , 43.5,\n",
              "       44. ])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cc-Fn23Q4AqQ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "gE78Nuog4Eg4",
        "outputId": "cf339d00-02e1-4d18-9c19-6066bb76cf77"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(perch_length, perch_weight)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('weight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dqSDbM-K4pkB"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    perch_length, perch_weight, random_state=42) # seed value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "seEljNAS4uET",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90e6b048-d28e-4e4c-d28d-8967f8bb8e55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4,)\n"
          ]
        }
      ],
      "source": [
        "test_array = np.array([1,2,3,4])\n",
        "print(test_array.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_array = test_array.reshape(2,2)\n",
        "print(test_array.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0p5_w4xcAeIi",
        "outputId": "5f9a7dc4-4d7b-4918-dc61-4a230c5d7f66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sC6HAwnnK4aU"
      },
      "outputs": [],
      "source": [
        "train_input = train_input.reshape(-1,1)\n",
        "test_input = test_input.reshape(-1,1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Og1eucsRwzIs",
        "outputId": "390f2995-5455-4bdd-a7ef-9267e9045deb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(42, 1) (14, 1)\n"
          ]
        }
      ],
      "source": [
        "print(train_input.shape, test_input.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtmNJ7OqrKy_"
      },
      "source": [
        "## 결정 계수 ($ R^2$)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BcPh-Da44lhx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c203231-ae0f-4b93-e614-3ca662c02cba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "knr = KNeighborsRegressor()\n",
        "\n",
        "knr.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEv88u6LIokr",
        "outputId": "8b1153a1-db11-40d8-b764-2cf9e883ffab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.992809406101064"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "knr.score(test_input, test_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R8Uju0xGLX3s"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKEf3y-5KVQx",
        "outputId": "c7a88c4f-ff25-4bec-e9a3-af6fa6de675e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19.157142857142862\n"
          ]
        }
      ],
      "source": [
        "# 테스트 세트에 대한 예측을 만듭니다\n",
        "test_prediction = knr.predict(test_input)\n",
        "# 테스트 세트에 대한 평균 절댓값 오차를 계산합니다\n",
        "mae = mean_absolute_error(test_target, test_prediction)\n",
        "print(mae)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLW8kdDv5asl"
      },
      "source": [
        "## 과대적합 vs 과소적합"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoXIfmiAJaNw",
        "outputId": "930583d9-2d35-4ae0-a651-b1cd7f97b52e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9698823289099254\n"
          ]
        }
      ],
      "source": [
        "print(knr.score(train_input, train_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jhu9abILLHjq",
        "outputId": "ceafb02d-0d3b-402b-92a9-9157a3a9841f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# 이웃의 갯수를 3으로 설정합니다\n",
        "knr.n_neighbors = 3\n",
        "# 모델을 다시 훈련합니다\n",
        "knr.fit(train_input, train_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cHedpJWCLVwb",
        "outputId": "abe385fe-d132-4257-818f-e1ed62522ee3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9746459963987609\n"
          ]
        }
      ],
      "source": [
        "print(knr.score(test_input, test_target))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-oQeMvC2NnY"
      },
      "source": [
        "## 확인문제"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        },
        "id": "ICPoeo9c2RLG",
        "outputId": "5f09078c-891c-4b67-fa47-604c592d58d8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dv0JGU7IQkay3NlLJjY+t5K2dB+ndI5fdB6spKS7g9isncuEpQ9h1oJpdB6r569odPLViO5VHaljyzh5eeju60kF+rjGxpIizR/enrt6pdzVBNaVkISJZq6aunt8seodfLnyb3j3z6JGbw6GjdQBEKqr4zpNruc2M6ZNLWBup5M/LyqiqiW6vrXd65eXw5fOOJyfHeH3zXu57ZTM1dc7Q43oxsaQozF8t4yhZiEhW2lh+gBsfW8XqskqmTxrO0k17qayqabRPVU0dc+aXMn1yCXPmlx5LFA2O1Nbz7Jr3eGXmhdHPNXWs3FbBgN49yGlhze3uSslCRLKKu3PfK1u4/bkN9OmZx11fPJPLJg5jzMxn4u4fqahizMxnaGldg0hF1bH3vfJz+cAJA1IQdfZTshCRrHGkpo4Zj6/mf1Zt52OnDOG2KycyqG9PAIYXFzT64o/V2gI4ud186vG20jgLEckK5fuPcNXdr/H06u3cfOl47vnKWccSBcCMaeMoCJ5oao86raXWJqpZiEjGWxup5Ot/WEZlVQ2//dJZTDt1aLN9pk8uAWDO/FK2V1S1WpuIVVJckMRIuy4lCxHJaM+t3cG/P7qKfoX5PP6NDzJh+HEt7jt9csmxpHH+7IUtNks1KMjPZca0cUmNt6tSM5SIZCR351cL3+Ybf3yD8cP6Mvf681tNFE3Fa5bKzzH6FeZjRGsUt1058VhykdapZiEiGedITR0z/7KauSu3M33ScGZ/5vRjI6zbqmmz1PDiAmZMG6fk0EFKFiKSdr9ZvJE/Ltna4vbDNXVUHK5hxrRx/J+pJ7Y6j1NrYpulkmHuiki3TT5KFiKSVhvLD/CT+aVMGlnMiYP6tLjfZROHcuH4zJlyY+6KCLOeWHNsYF+koopZT6wB6BYJQ8lCRNJq9l9L6d0jj3u+MoUBfXomPiBDxBsBHjtCvKtTB7eIpM3STXt4Yf1OvjH1xKxKFADbW3iyqqXyrqZNycLMbmhLmYhIS9ydH/11A8OKenHth8aEHU67DW9hPEZL5V1NW2sW18Qp+2oS4xCRLu6ZNTtYta2CGy8+ud1PNmWCeI/idqdxGq32WZjZ1cAXgDFmNi9mU19gbyoDE5Gu42htPT9+rpTxQ/ty5Zkjwg6nQ7r7o7iJOrhfBXYAA4GfxpQfAFanKigR6VoeWrqVd/ce5oGvnU1uFk/9nexHcbNJq8nC3bcCW4Hz0hOOiHQ1+4/U8IsX3+b8kwbw0ZMHhR2OdFBbO7ivNLO3zazSzPab2QEz29/Ri5pZsZk9bmYbzGy9mZ1nZv3NbEFwnQVm1i/Y18zsF2a20cxWm9mZHb2uiKTfbxe/w77DNcy67JQOD66T8LW1g/vHwOXuXuTux7l7X3dv+yQtzd0JPOfu44EzgPXATOBFdx8LvBh8BrgMGBu8rgPu6sR1RSSNtldUce/Lm/n05BJO0zKlWa2tyWKnu69PxgXNrAj4CHAvgLsfdfcK4ArgwWC3B4HpwfsrgD941GtAsZkNS0YsIpJaP1vwFu5w0yUnhx2KdFKip6GuDN4uM7NHgblAdcN2d3+iA9ccA+wC7jezM4DlwA3AEHffEezzHtAwzr8E2BZzfFlQtiOmDDO7jmjNg1GjRnUgLBFJpvU79vOXN8r4+odPYES/wrDDkU5K9DTUp2LeHwYuifnsQEeSRR5wJvCv7r7UzO7k/San6Ind3czatXyVu98N3A0wZcoULX0lErLZf93Acb3y+ebUk8IORZIg0dNQX0vBNcuAMndfGnx+nGiy2Glmw9x9R9DMVB5sjwAjY44fEZSJSIZ6deNu/vbWLv7j46dQVJgfdjiSBG2aSNDMfhGnuBJY5u5PteeC7v6emW0zs3HuXgpcBKwLXtcAs4OfDeedB1xvZo8A5wKVMc1VIpKBnlmzg7498/jyeceHHYokSVtnne0FjAf+HHz+DLAZOMPMLnD3b7Xzuv8KPGRmPYBNwNeIdrY/ZmbXEh3b8blg32eBjwMbiTaFpaK2IyJJtKqsgtNHFmXltB4SX1uTxenA+e5eB2BmdwEvAR8C1rT3ou6+EpgSZ9NFcfZ14JvtvYaIhONITR0bdhzg6x85ISnn684LDmWStiaLfkAfok1PAL2B/u5eZ2bVLR8mIt3N+h37qa13zhjR+XEV3X3BoUzSnkF5K83sfjN7AFgBzDGz3sALqQpORLLP6rLo35Snjyju9LlaW3BI0qtNNQt3v9fMngXOCYq+4+7bg/czUhKZiGSlVWUVDOzTk2FFvTp9ru6+4FAmabVmYWbjg59nAsOIDo7bBgzVHE0iEs/qskomjSxKyjxQ3X3BoUySqGZxI9FR0T+Ns82BC5MekYhkrQNHanhn10EuP2N4Us43Y9q4Rn0W0L0WHMokiQblXRf8vCA94YhINlsTqcQdTk9C5zZowaFM0tZBeYVEaxmj3P06MxsLjHP3p1ManYhklWR2bjfozgsOZZK2Pjp7P9EJ/z4YfI4QHaCnZCEix6wuq2Bk/wL69+7RbJvGS2S3tj46e6K7/xioAXD3w4BWMRGRRlZtq4xbq2gYLxGpqMJ5f7zE3BWa5i1btDVZHDWzAqKd2pjZicRMVS4isvtgNZGKKibFSRYaL5H92toMdQvwHDDSzB4Czge+mqqgRCT7rC6rAOJ3bmu8RPZra7K4BniG6HTim4Ab3H13yqISkayzalslOUbc5VOHFxcQiZMYNF4ie7S1GepeojPPXg78Evidmd2QsqhEJOusLqvgpMF96N2z+d+gM6aNo6DJDLQaL5Fd2jrdxyIz+ztwNnAB8A3gVODOFMYmIlnC3VldVskF4wfH3a7xEtmvreMsXiQ60+wSolOTn+3u5a0fJSLdRaSiij2HjrY606zGS2S3tjZDrQaOAqcRXdvitODpKBERVm2LDsY7Y2TyBuNJZmlrM9S/A5hZX6JPQd0PDAV6piwyEckaq8sq6JGbw/ihx4UdiqRIW5uhrgc+DJwFbAHuI9ocJSLCqrIKThnWlx55bW2skGzTnjW4fwYsd/faFMYjIlmmvt5ZG9nPp9Uf0aW1tRnqJ6kORESy06bdBzlYXZu0mWYlM6nOKCKdos7t7kHJQkQ6ZVVZBb175HLioD5hhyIppGQhIp2yqqyS00qKyM3RRNRdmZKFiHTY0dp61m/fryaobkDJQkQ6rPS9Axytq1fndjegZCEiHbYqmJb8jCQuoyqZSclCRDpsdVkF/QrzGdFPs/90dUoWItJhq7ZVcsbIYszUud3VhZYszCzXzFaY2dPB5zFmttTMNprZo2bWIyjvGXzeGGwfHVbMIvK+w0drebv8QNw1t6XrCbNmcQOwPubz7cAd7n4SsA+4Nii/FtgXlN8R7CciIVsb2U+90+q05NJ1hJIszGwE8Ang98FnAy4kumwrwIPA9OD9FcFngu0Xmeq8IqF7f81t1Sy6g7BqFj8Hvg3UB58HABUxkxSWAQ2zkpUA2wCC7ZXB/o2Y2XVmtszMlu3atSuVsYsI0cF4w4t6MaivViroDtKeLMzsk0C5uy9P5nnd/W53n+LuUwYNGpTMU4tIHKu2VahW0Y2EUbM4H7jczLYAjxBtfroTKDazhllwRwCR4H0EGAkQbC8C9qQzYBFpbN+ho7y797BGbncjaU8W7j7L3Ue4+2jg88BCd/8isAj4bLDbNcBTwft5wWeC7Qvd3dMYsog08fe3o0296tzuPjJpnMXNwI1mtpFon8S9Qfm9wICg/EZgZkjxiQhwsLqW257dwPihfTl7TP+ww5E0aetKeSnh7ouBxcH7TcA5cfY5AvxTWgMTkRb99PlSdh44wm++dCb5uZn096akkv5Li0ibrSmr5MFXt/DFc0dx5qh+YYcjaaRkISJtUltXz6wnVzOgT09mTBsfdjiSZkoWItImf1iylbWR/dzyqQkUFeSHHY6kmZKFiCS0vaKKnz5fytRxg/jExGFhhyMhULIQkYS+N+9N6tz5wRWnaYbZbkrJQkRa9fyb7/H8up3ccNHJjOxfGHY4EhIlCxFp0cHqWm6Z9ybjhvTlf314TNjhSIhCHWchIpntjgVvsaPyCL/6gsZUdHf6ry8ica2NVHL/K5v5wrmjOOt4jano7lSzEOlG5q6IMGd+KdsrqhheXMCMaeOYPrmk2X519c53nlxD/949uVljKgQlC5FuY+6KCLOeWENVTR0AkYoqZj2xBqBZwrj35U2sLqvkF1dPpqhQYypEzVAi3cac+aXHEkWDqpo65swvbVS2sfwgP3n+LS6ZMIRPna4xFRKlZCHSTWyvqEpYXlfvzHh8FQX5udz6aY2pkPcpWYh0E8OLCxKW3/fyZla8W8H3Lz+VwX17pSs0yQJKFiLdxIxp4yjIz21UVpCfy4xp4wB4Z9dBfvJ8KRdPGMIVk4aHEaJkMHVwi3QTDZ3Y8Z6Gqqt3vv34anrl5/LD6Wp+kuaULES6gLY+Ejt9cknc8vtf2czyrfu446ozGHycmp+kOSULkSw3d0WEGY+voqYuujR9pKKKGY+vApo/EhvPpl0HmTO/lI+dMpjpkxLvL92T+ixEstz3/+fNY4miQU2d8/3/eTPhsQ3NTz3zcvjRpyeq+UlapJqFSJbbd7imXeWxHnh1C8u27uNnn1Pzk7RONQuRbmrz7kPMmb+Bi8YP5tNtaK6S7k01C5EsdKi6lt/97R1eWF9OjkG9N98nx+CyO19q8Ry7DlTTIzeHH12p5idJTMlCJIvU1zt/eaOMOfNLKT9QzXknDGDC8ON4M7Kf2HxhwIThxzGsKP5APICR/Qr4ynmjGaLmJ2kDJQuRFGnr46xt9Y/Ne/nB0+tYE6lk0shi7vrSWcemDk/2tUSaUrIQSYH2zPCayLa9h7n+4TdYVVYJQL/CfL5y3vGN1phoafyESLKog1skBdo6w2trqo7WcftzG5j6k8XHEgVEn3L6jyfXMndFJGnxiiSiZCGSAm2Z4bU1pe8d4PJfvcxdi9+hR5zlTNubeEQ6S8lCJAXaMsNrPO7Ow0vf5fJfvcy+wzX897XncKRJDaVBWxOPSDKkPVmY2UgzW2Rm68zsTTO7ISjvb2YLzOzt4Ge/oNzM7BdmttHMVpvZmemOWaS9Es3wGs/DS7cy4T/n850no30bN1x0Eh8eO6jDiUckmcKoWdQCN7n7BOADwDfNbAIwE3jR3ccCLwafAS4Dxgav64C70h+ySPtMn1zCbVdOpKS4AANKigu47cqJLXZC//yFt/iPJ9ce6+eorq3nR89uiM771IHEI5JsaX8ayt13ADuC9wfMbD1QAlwBTA12exBYDNwclP/B3R14zcyKzWxYcB6RjNWWJ5Tq6527X9rEz194u9m2hn6JV2ZeCMSfWlwkXUJ9dNbMRgOTgaXAkJgE8B4wJHhfAmyLOawsKGuULMzsOqI1D0aNGpWymEWSZXVZBXPml/LS27tb3KehX0KPxkrYQksWZtYH+AvwLXffHzvdgLu7mcWZwKBl7n43cDfAlClT2nWsSLrU1zsLN5Rzz0ubWLp5Lw3/1+cA9XH2V7+EZIpQkoWZ5RNNFA+5+xNB8c6G5iUzGwaUB+URYGTM4SOCMpGM0too6iM1dTzxRoTfv7yJTbsO0a8wn7wcozaY1CleolC/hGSStCcLi1Yh7gXWu/vPYjbNA64BZgc/n4opv97MHgHOBSrVXyGZJt6I7Zl/WU1lVQ37Dh/lv5dsZc+ho0wsKeLOz0/i9r9uiDuFeK4Z9e7ql5CME0bN4nzgy8AaM1sZlH2HaJJ4zMyuBbYCnwu2PQt8HNgIHAa+lt5wRVrm7uw9dJRbn1nXbMT2kdp6bpkXXYDoovGD+fpHTuDcMf0xM771yMp4p6Penc2zP5HyuEXaK4ynoV4GWpoP+aI4+zvwzZQGJdIBR2rq+PoflrXaQQ3wwo0f5aTBfRqVDS8uIBJnUJ36KCRTaSJBkTaK7ZMYVtQLM4hUHAFa7qAuKS5oliggOmgvttkK1EchmU3JQqQNmvZJbK880mh7vESRn2scqq5lzMxnmvVBNPzU2AnJFkoWIm0QbxbZeBo6qIsL8zl4pJaKqmgndrwpyjV2QrKJJhIUacHcFRHOn72QMTOfidu/EE9DB3Vhjzxqmqx1qpliJZupZiESx4YT4qYAAAnCSURBVNwVEWb8eVWzL/xEGjqoOztFuUimUc1CJI7vzXuz3YkitoNaM8VKV6NkIRJHQ19DW/UrzG80q6xmipWuRs1Q0m19d+4a/rR0G3Xu5Jpx9bkjuXX6xITH/fyqSQmfYtLTTtLVWHTMW9cyZcoUX7ZsWdhhSAb77tw1/PG1d5uVnzykDzlmbHjvQIvHbtEIa+mizGy5u0+Jt03NUNIt/Wnptrjlb+08yKC+PdMcjUjmU7KQbsXdWb51L3Wt1Kj/+9pz0xiRSHZQn4V0C0dq6nh69Q4eeHUzayP7W9wvN1hXpTA/h8M1zcdlF+br7yvpnpQspEtpuqbEWccXs3DDLg5W1wIw9Lhe3Dr9NNZEKnj09bJmx199bnTplB9deTo3PraS2KdncyxaLtIdKVlI1olNCEUF+ZhBxeGaY1NsNIyPiFRUNRt5XXH4KH165nH7Z84gPzenxaeh9DSTSGN6GkqyStMJ/TqipLiAV2ZemMSoRLqG1p6GUs1CMkbTKcD/+UNjGNS3J6u2VbLnUDUAz7+5s1OJAjTlhkhHKFlIRnh46Va+N28dR+uincrbK49w6zPrAeiZl8PQol4YdDpRgKbcEOkIJQsJxZ6D1fxj816WbNrDa5v28NbOg3H3G9SnJ6/OupD83OhTSOfPXtjmGWDj0ZQbIh2jZBHD3alr5+Rx0jaVVTW8vmUvS97Zw2ub9lK6MzpCurBHLlNG928xWew+WH0sUUD8FeZi5ecavXvkUVlVw/DiAi4YP4hFG3apk1qkk5QsYqwuq+SKX78SdhhdWkF+LlNG9+PyScP5wAkDOH1EEfm5OS3WGJo2GTV9Sin2aSglA5HUUbKIMbSoFzddfHLYYWSd9Tv289LG3Rw4UkvfXnl8+KSBnDLsuEb79MrP5czji5lYUkyPvOYD29qzJrVWmBNJPyWLGEOO68W/XjQ27DCyytwVEX6z+J1jX/IHjtSyqHQXl5w6tF1f6BrXIJLZlCwkrqYjoVv64o63NnXD8qHt/aJXjUEkcylZSDNNB75FKqqY9cQagGZf5lo+VKR70Kxo0kxrtYWmtHyoSPegZCHNtKe2oOVDRboHJQtppj21hemTS7jtyomUFBdgROddil2LWkS6BvVZSDPteYwV1DEt0h1kTc3CzC41s1Iz22hmM8OOpytTbUFEmsqKmoWZ5QK/Bi4GyoDXzWyeu68LN7KuS7UFEYmVLTWLc4CN7r7J3Y8CjwBXhByTiEi3kS3JogTYFvO5LCg7xsyuM7NlZrZs165daQ1ORKSry5ZkkZC73+3uU9x9yqBBg8IOR0SkS8mWZBEBRsZ8HhGUiYhIGmRLsngdGGtmY8ysB/B5YF7IMYmIdBvmnh2L/ZjZx4GfA7nAfe7+w1b23QVs7cTlBgK7O3F8Kim2jlFsHaPYOiZbYzve3eO242dNskgnM1vm7lPCjiMexdYxiq1jFFvHdMXYsqUZSkREQqRkISIiCSlZxHd32AG0QrF1jGLrGMXWMV0uNvVZiIhIQqpZiIhIQkoWIiKSkJJFDDPbYmZrzGylmS0LOZb7zKzczNbGlPU3swVm9nbws18GxfY9M4sE925lMC4mjNhGmtkiM1tnZm+a2Q1Beej3rpXYQr93ZtbLzP5hZquC2L4flI8xs6XB0gCPBoNiMyW2B8xsc8x9m5Tu2GJizDWzFWb2dPA59PvWSmwdum9KFs1d4O6TMuAZ6QeAS5uUzQRedPexwIvB5zA8QPPYAO4I7t0kd382zTE1qAVucvcJwAeAb5rZBDLj3rUUG4R/76qBC939DGAScKmZfQC4PYjtJGAfcG0GxQYwI+a+rQwhtgY3AOtjPmfCfWvQNDbowH1TsshQ7v53YG+T4iuAB4P3DwLT0xpUoIXYMoK773D3N4L3B4j+IykhA+5dK7GFzqMOBh/zg5cDFwKPB+Vh3beWYssIZjYC+ATw++CzkQH3LV5snaFk0ZgDz5vZcjO7Luxg4hji7juC9+8BQ8IMJo7rzWx10EwVShNZLDMbDUwGlpJh965JbJAB9y5orlgJlAMLgHeACnevDXZptjRAWLG5e8N9+2Fw3+4ws55hxEZ0GqJvA/XB5wFkyH2jeWwN2n3flCwa+5C7nwlcRrSJ4CNhB9QSjz7znDF/XQF3AScSbSbYAfw0zGDMrA/wF+Bb7r4/dlvY9y5ObBlx79y9zt0nEZ3V+RxgfBhxxNM0NjM7DZhFNMazgf7AzemOy8w+CZS7+/J0XzuRVmLr0H1Tsojh7pHgZznwJNF/MJlkp5kNAwh+locczzHuvjP4B10P3EOI987M8ol+GT/k7k8ExRlx7+LFlkn3LoinAlgEnAcUm1nD8suhLw0QE9ulQbOeu3s1cD/h3LfzgcvNbAvRFTwvBO4kM+5bs9jM7I8dvW9KFgEz621mfRveA5cAa1s/Ku3mAdcE768BngoxlkYavogDnyakexe0F98LrHf3n8VsCv3etRRbJtw7MxtkZsXB+wKi692vJ/rF/Nlgt7DuW7zYNsQkfyPaJ5D2++bus9x9hLuPJrp0wkJ3/yIZcN9aiO1LHb1veYl36TaGAE9G7x95wMPu/lxYwZjZn4CpwEAzKwNuAWYDj5nZtUSnYP9cBsU2NXgEz4EtwL+EERvRv6a+DKwJ2rgBvkNm3LuWYrs6A+7dMOBBM8sl+kfkY+7+tJmtAx4xs1uBFUSTXabEttDMBgEGrAS+EUJsLbmZ8O9bSx7qyH3TdB8iIpKQmqFERCQhJQsREUlIyUJERBJSshARkYSULEREJCElC5EOMLODifdq9zknxc44a9HZaP9vsq8j0hFKFiKZYxIQytTuIokoWYh0kpnNMLPXg4nZGtZaGG1m683snmANhueD0ceY2dnBvivNbI6ZrQ3WO/gv4Kqg/Krg9BPMbLGZbTKzfwvpVxRRshDpDDO7BBhLdH6dScBZMRNQjgV+7e6nAhXAZ4Ly+4F/CSbGqwNw96PAfwKPBmsMPBrsOx6YFpz/lmBuKZG0U7IQ6ZxLgtcK4A2iX+5jg22bYxaWWQ6MDuY46uvuS4LyhxOc/xl3r3b33UQnP8y0aemlm9DcUCKdY8Bt7v67RoXR9SqqY4rqgIIOnL/pOfRvVkKhmoVI58wH/jlYowIzKzGzwS3tHEyxfcDMzg2KPh+z+QDQN2WRinSCkoVIJ7j780SbkpaY2RqiS2km+sK/FrgnmHm2N1AZlC8i2qEd28EtkhE066xImplZn4Y1pc1sJjDM3W8IOSyRVqn9UyT9PmFms4j++9sKfDXccEQSU81CREQSUp+FiIgkpGQhIiIJKVmIiEhCShYiIpKQkoWIiCT0/wFpJkS1qunv9QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# k-최근접 이웃 회귀 객체를 만듭니다\n",
        "knr = KNeighborsRegressor()\n",
        "# 5에서 45까지 x 좌표를 만듭니다\n",
        "x = np.arange(5, 45).reshape(-1, 1)\n",
        "\n",
        "# n = 1, 5, 10일 때 예측 결과를 그래프로 그립니다.\n",
        "for n in [1, 5, 10]:\n",
        "    # 모델 훈련\n",
        "    knr.n_neighbors = n\n",
        "    knr.fit(train_input, train_target)\n",
        "    # 지정한 범위 x에 대한 예측 구하기 \n",
        "    prediction = knr.predict(x)\n",
        "    # 훈련 세트와 예측 결과 그래프 그리기\n",
        "    plt.scatter(train_input, train_target)\n",
        "    plt.plot(x, prediction)\n",
        "    plt.title('n_neighbors = {}'.format(n))    \n",
        "    plt.xlabel('length')\n",
        "    plt.ylabel('weight')\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "3-1_K-Nearest_Neighbor",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.8 on Backend.AI",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}