3-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-최근접 이웃 회귀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {
    "colab_type": "text",
    "id": "i5J2cFzCrDWT"
   },
   "source": [
    "## 데이터 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fL3wuWxD0cH6"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "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",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cc-Fn23Q4AqQ"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1097,
     "status": "ok",
     "timestamp": 1587904061347,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "gE78Nuog4Eg4",
    "outputId": "e2862f05-14c9-4445-b711-1326aca5c020"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(perch_length, perch_weight)\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('weight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dqSDbM-K4pkB"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "seEljNAS4uET"
   },
   "outputs": [],
   "source": [
    "train_input, test_input, train_target, test_target = train_test_split(\n",
    "    perch_length, perch_weight, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1391,
     "status": "ok",
     "timestamp": 1587904061649,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "sC6HAwnnK4aU",
    "outputId": "55cc21c4-0934-4244-ca96-2c69a099f345"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42,) (14,)\n"
     ]
    }
   ],
   "source": [
    "print(train_input.shape, test_input.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1666,
     "status": "ok",
     "timestamp": 1587904061928,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "Og1eucsRwzIs",
    "outputId": "fbfbad2f-99ed-43f7-aedb-d0cf67410c96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n"
     ]
    }
   ],
   "source": [
    "test_array = np.array([1,2,3,4])\n",
    "print(test_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1660,
     "status": "ok",
     "timestamp": 1587904061929,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "y-DXX-xtw8Jb",
    "outputId": "c423952b-c355-4155-b3b4-b45b7c445715"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2)\n"
     ]
    }
   ],
   "source": [
    "test_array = test_array.reshape(2, 2)\n",
    "print(test_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2z-LC4zrxzWL"
   },
   "outputs": [],
   "source": [
    "# 아래 코드의 주석을 제거하고 실행하면 에러가 발생합니다\n",
    "# test_array = test_array.reshape(2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_GfrND5GKg_z"
   },
   "outputs": [],
   "source": [
    "train_input = train_input.reshape(-1, 1)\n",
    "test_input = test_input.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1655,
     "status": "ok",
     "timestamp": 1587904061930,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "0c8e0UrkLJDe",
    "outputId": "dfa7fb53-eb97-4bfc-de5b-89391c07d835"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 1) (14, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_input.shape, test_input.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NtmNJ7OqrKy_"
   },
   "source": [
    "## 결정 계수 ($ R^2$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BcPh-Da44lhx"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1647,
     "status": "ok",
     "timestamp": 1587904061930,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "oe7MTnof45sP",
    "outputId": "0aa0f807-2c16-46b2-fc01-f96af997e7a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr = KNeighborsRegressor()\n",
    "# k-최근접 이웃 회귀 모델을 훈련합니다\n",
    "knr.fit(train_input, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1644,
     "status": "ok",
     "timestamp": 1587904061931,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "yEv88u6LIokr",
    "outputId": "1ff7d16b-0231-45ee-e9c8-86c739f2b38c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.992809406101064"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knr.score(test_input, test_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R8Uju0xGLX3s"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1637,
     "status": "ok",
     "timestamp": 1587904061931,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "QKEf3y-5KVQx",
    "outputId": "c083b9d6-6836-4980-fcb7-0723e0cbcf86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": {
    "colab_type": "text",
    "id": "pLW8kdDv5asl"
   },
   "source": [
    "## 과대적합 vs 과소적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1634,
     "status": "ok",
     "timestamp": 1587904061932,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "ZoXIfmiAJaNw",
    "outputId": "99289bfd-0735-4f96-874b-e52dda1725c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9698823289099254\n"
     ]
    }
   ],
   "source": [
    "print(knr.score(train_input, train_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1628,
     "status": "ok",
     "timestamp": 1587904061932,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "Jhu9abILLHjq",
    "outputId": "7fa6a8cf-1137-4ec6-e3df-14acfcb6be4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9804899950518966\n"
     ]
    }
   ],
   "source": [
    "# 이웃의 갯수를 3으로 설정합니다\n",
    "knr.n_neighbors = 3\n",
    "# 모델을 다시 훈련합니다\n",
    "knr.fit(train_input, train_target)\n",
    "print(knr.score(train_input, train_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1623,
     "status": "ok",
     "timestamp": 1587904061932,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "cHedpJWCLVwb",
    "outputId": "862421ff-c2f8-4b83-9724-025ff6afadd7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9746459963987609\n"
     ]
    }
   ],
   "source": [
    "print(knr.score(test_input, test_target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z-oQeMvC2NnY"
   },
   "source": [
    "## 확인문제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 851
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2364,
     "status": "ok",
     "timestamp": 1587904062678,
     "user": {
      "displayName": "Haesun Park",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhsWlS7sKQL-9fIkg3FmxpTMz_u-KDSs8y__P1ngQ=s64",
      "userId": "14935388527648823821"
     },
     "user_tz": -540
    },
    "id": "ICPoeo9c2RLG",
    "outputId": "2f7e7afc-3d3b-46fd-d8ee-83b3f16fbc55"
   },
   "outputs": [
    {
     "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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3i7+pDXBytcxHUjTPQkQywtY9B7jukZWcMOAYvn/BmEbHZ08dRUELhsTWai+1pKhmISJpr6qmlq89uIw6d+68ckLMeRL1/Q71o52STQFFhQWtGGn7pWQhImnvRwve4a1t5dz1xVMY0qtL3POmjy86nDSaapaqV5Cfy+ypo1o11vZKzVAiktbmLy/h/te38OVPHse5Y5LfxS5Ws1R+jtGjcz5GpEZxy4yxGgmVJNUsRCRtvbergpvmrWLi0J58u5k1gIbNUpqEd3SULEQkLe2rquEr9y+lS8c8fnPF+BYtJR7dLNUaomeIZ1vyUbIQkbTj7tw0bxUbP9jP/f9vEv2O6RR2SFk/A1x9FiKSdv70+mb+unI73zp3FKcf3zvscICmZ4BnAyULEUkr5QeqmfO3tZwxsg9fPeP4sMM5bHuckVXxytubpJKFmV2bTJmIyNF68I0tHDhUyw3TRqfV3tgD48zHiFfe3iRbs5gZo+yqVoxDRIRDNXXc9+pGpgzvxQkDjwk7nCPEGoqbTfM0muzgNrPLgSuAYWb2ZNShbsCetgxMRLLPU6u2s+ujqkbrPqWDbB+Km2g01KvADqA38Iuo8grgrbYKSkSyj7vzh5c2MrxvV84Y0SfscGJq7aG4maTJZOHum4HNwOTUhCMi2er1DXtYvf0jbpkxNq36KiQi2Q7uGWb2npmVm9lHZlZhZh+19KZmVmhmj5nZWjNbY2aTzaynmT0X3Oc5M+sRnGtm9mszW29mb5nZhJbeV0TS190vb6BXlw5clKV/uae7ZDu4fwZc4O7d3f0Yd+/m7kfT+/Qr4Bl3Hw2cDKwBbgSed/cRwPPBe4DPACOCn1nAnUdxXxFJQ++X7uPva3Zz5WlDYq4oK+FLNlnscvc1rXFDM+sOfBK4G8DdD7l7GXAhMDc4bS4wPXh9IfBHj3gdKDSzAa0Ri4ikh3te3kiHvByuPG1I2KFIHIlGQ80IXi4xs0eA+UBV/XF3n9eCew4DSoF7zexkYClwLdDP3XcE5+wE+gWvi4CtUZ/fFpTtiCrDzGYRqXkwePDgFoQlImHYs/8Qf1m2jYvGFdGnW8eww5E4Eo2G+reo1weAc6PeO9CSZJEHTAD+y90Xm9mv+FeTU+TC7m5mzdq+yt3vAu4CKC4u1tZXIhniwcWbOVhdxzWfGBZ2KNKERKOhrm6De24Dtrn74uD9Y0SSxS4zG+DuO4Jmpt3B8RJgUNTnjw3KRCTDVdXUMve1zXxyZB9G9usWdjjShKRWnTWzX8coLgeWuPsTzbmhu+80s61mNsrd1wHnAO8EPzOBOcHv+us+CXzdzB4GJgHlUc1VIpLBnlyxndKKKm67RLWKdJfsEuWdgNHAo8H7zwEbgZPN7Cx3/2Yz7/tfwANm1gHYAFxNpLP9z2Z2DZG5HZcE5z4NnAesJ9IU1ha1HRFJMXfn7pc3MqpfNz4+PD1WlpX4kk0WJwFT3L0WwMzuBF4CPg6sau5N3X0FUBzj0DkxznXga829h4ikt1fWf8janRX87OKTMIs/CS+bNxxKJ8kmix5AVyJNTwBdgJ7uXmtmVfE/JiIS2x9e3kDvrh25cNzAuOdk+4ZD6aQ5k/JWmNm9ZnYfsBy41cy6AH9vq+BEpH16b1cFL64rZebkIXTMiz8JL9s3HEonSdUs3P1uM3samBgUfdfdtwevZ7dJZCKSsRa8tZ2fPLWGOo89iv3AoVo65uXwhQST8LJ9w6F0kmhS3mh3Xxu1HlP95Lj+Ztbf3Ze1bXgikmnq6pzbn3uX3BzjjOHxV4897bhe9OzSoclrDSwsoCRGYsiWDYfSSaKaxfVEZkX/IsYxB85u9YhEJKO9tP4D3i/dz22XnMyMCcce1bVmTx11RJ8FZNeGQ+kk0aS8WcHvs1ITjohkuntf2Ujvrh05/6SjX8It2zccSifJTsrrTKSWMdjdZ5nZCGCUuy9o0+hEJKNsKN3Hi+tK+eanRjTZcd0c2bzhUDpJdujsvUQW/Ds9eF9CZIKekoWIHDb31U3k5xpXTGq8mKfmS2S2ZIfOHu/uPwOqAdz9AKCtrETksI8OVvPY0m3820kD6dut0xHH6udLlJRV4vxrvsT85VrmLVMkmywOmVkBkU5tzOx4opYqFxF5dMk29h+q5eopjdd50nyJzJdsM9TNwDPAIDN7AJgCXNVWQYlIZqmtc+a+uolThvRg7LHdGx3XfInMl2zNYibwFPBD4EGg2N1fbKugRCSzLFq7my17DnD1lKExj8ebF6H5Epkj2WRxN5GVZy8AfgP8n5ld22ZRiUhGufeVjQzo3ompY/rHPD576igKGuytrfkSmSXZ5T5eMLN/AqcCZwFfAcYAv2rD2EQkA6zbWcGr73/Id6aNIj839t+fmi+R+ZKdZ/E8kZVmXyOyNPmp7r676U+JSDa479WNdMzL4fJTGw+Xjab5Epkt2Waot4BDwIlE9rY4MRgdJSJZbO/+Q8xbVsJF44vokWCdJ8lsyTZDXQdgZt2IjIK6F+gPdGyzyEQk7T385laqauq4Kk7HtrQfyTZDfR34BHAKsAm4h0hzlIhkqZraOv702iYmH9eL0f2PCTscaWPN2YP7NmCpu9e0YTwikiEWrt7F9vKDfP+CMWGHIimQbDPUz9s6EBHJLPe+spFBPQs452P9wg5FUiDZDm4RkcPeLilnyea9zJw8lNwcLROXDZQsRKTZHli8hU75OXy+eFDYoUiKKFmISLPsr6rhyRUlfPakgXQvyA87HEkRJQsRaZa/rtzO/kO1XD5RtYpsomQhIs3y0BtbGNmvKxMG9wg7FEkhJQsRSdrbJeWs3FbO5RMHY6aO7WyiZCEiSXv4zS10zMvhIq3xlHWULEQkKQcO1TB/+XbOHzuAws5aByrbhJYszCzXzJab2YLg/TAzW2xm683sETPrEJR3DN6vD44PDStmkWy2YOUO9lXVcPmkpleXlfYpzJrFtcCaqPc/BW539+HAXuCaoPwaYG9Qfntwnoik2ENvbmF4364UD1HHdjYKJVmY2bHA+cAfgvcGnA08FpwyF5gevL4weE9w/BxTz5pISq3Z8RHLt5Rx2amD1LGdpcKqWfwS+A5QF7zvBZRFLVK4DajvQSsCtgIEx8uD849gZrPMbImZLSktLW3D0EWyz8NvbKFDbg6fm3Bs2KFISFKeLMzss8Bud1/amtd197vcvdjdi/v06dOalxbJapWHapm3vITPjO2vDY6yWLJLlLemKcAFZnYekaXPjyGyl3ehmeUFtYdjgZLg/BJgELDNzPKA7sCHqQ9bJDs9tWoHFQdruHyiOrazWcprFu5+k7sf6+5DgcuARe7+BeAF4OLgtJnAE8HrJ4P3BMcXubunMGSRrPbwG1s4rncXJg3rGXYoEqJ0mmdxA3C9ma0n0idxd1B+N9ArKL8euDGk+ESyzru7KliyeS+XTVTHdrYLoxnqMHd/EXgxeL0BmBjjnIPA51MamIgAkXWg8nNNHduSVjULEUkjB6trmbeshKlj+tOra8eww5GQKVmISEx/e3sH5ZXVXKGObUHJQkTieOiNrQzt1ZnTjms0rUmykJKFiDTy3q4K3ti4h0tPHUyO9tgWlCxEJIY/vraZDrk5XFKsjm2JULIQkSNUHKxm3rJtfPbkAerYlsOULETkCPOWlbD/UC0zJw8NOxRJI0oWInKYuzP3tU2cPKiQkwcVhh2OpBElCxE57JX1H7KhdD8zJw8JOxRJM6HO4BaR1Jq/vIRbF65je1klAwsLmD11FNOj9tOe+9omenXpwHljB4QYpaQjJQuRLDF/eQk3zVtFZXUtACVlldw0bxUA08cXsW3vAZ5fs4uvnnk8nfJzwwxV0pCaoUSyxK0L1x1OFPUqq2u5deE6AB5YvAWAKyapCUoaU7IQyRLbyyrjlh+sruXhN7bw6RP6UVRYkOLIJBMoWYhkiYFxksDAwgIWvLWDvQeqNVxW4lKyEMkSs6eOoqBBX0RBfi7fPnckc1/dxPC+XZl8vNaBktiULESyxPTxRdwyYyxFhQUYUFRYwC0zxjK0dxdWlZQzc/IQbXAkcWk0lEg7kGhIbL3p44salV//yAq6dszjIm1wJE1QshDJcPOXlzD7sZVU10a2pi8pq2T2YysBYiaMaB/sq2LBWzu4fOIgunbU14HEp2YokQz3g7+uPpwo6lXXOj/46+qEn33kza0cqq3ji+rYlgSULEQy3N4D1c0qr1dTW8f9r2/m48N7M7xv17YITdoRJQuRLPX3NbvYUX6Qf9c6UJIENVKKZLjCgnzKKhvXIgryc/jSfW/G/dy6nRUUFRZwzsf6tWV40k4oWYhkuO9fMIbZj66kuu5f/RYGVFbXsfnD/XTuEPufea+uHbjm48PI1bapkgQlC5E2kuxw1qNVf81bF66jpKySjnk5VNXU8Y1zRnDdp0Zo7oS0CiULkTaQaIXXllyvqcQzfXwRxUN7cPW9b7Lpw/38/PMnc/EpmjchrUcd3CJtINEKr81Rn3hKyipx/pV45i8vOXzOyq1lTL/jVXZ+dJC5X5qoRCGtTslCpA00tcJrcyVKPM+u3smld71Gp/wcHv/P0zn9+N7ND1gkASULkTbQ1AqvzRUvwZSUVXL3yxv58v1LGdX/GB7/zykM79ut2dcXSUbK+yzMbBDwR6Af4MBd7v4rM+sJPAIMBTYBl7j7Xov0zv0KOA84AFzl7stSHbdIc8yeOuqIPguIrPA6e+qouJ+p75coKaukZ5cODOpRwPrd+/C4n4AfLXiHqWP68ctLx1PQQbvbSdsJo4O7BviWuy8zs27AUjN7DrgKeN7d55jZjcCNwA3AZ4ARwc8k4M7gt0jaih6hlMxoqMeXbePGv6yiqrYOgD37D7Fn/yEmH9eLgg65/PPdUmqihsbm5RifHNmHs0b35YqJgzX8VdpcypOFu+8AdgSvK8xsDVAEXAicGZw2F3iRSLK4EPijuzvwupkVmtmA4DoiaSvWCq8N7Siv5PHlJdz27LtHJIN6W/Yc4JUbz07ZMFyReEIdOmtmQ4HxwGKgX1QC2EmkmQoiiWRr1Me2BWVHJAszmwXMAhg8eHDbBS3SSh5cvIWbn3y70SKA0er7K5JJPCJtKbQObjPrCvwF+Ka7fxR9LKhFNNVU24i73+Xuxe5e3KdPn1aMVKR1VdfW8b35q/ju46vICSbM5caZONeSDnGRthBKzcLM8okkigfcfV5QvKu+ecnMBgC7g/ISYFDUx48NykTSSjJNRR/uq+KrDyzjjY17yMsxqmoifRS13vhvo0Qd4iKplPKaRTC66W5gjbvfFnXoSWBm8Hom8ERU+b9bxGlAuforJN0kM3Fu9fZyLvjtK6zcWkaPzvkx+yhyzY7Y8lRNT5IuwqhZTAG+CKwysxVB2XeBOcCfzewaYDNwSXDsaSLDZtcTGTp7dUqjFUlCUxPnpo8vYsFb2/n2oyvp0bkDj35lMhf+9pWY16lzZ+Oc81MRskizhDEa6mUii2LGck6M8x34WpsGJXKUmpo49/OF6/jtC+s5ZUgP7rxyAn27dWJgYQElMT6jPgpJV1pIUCRJDfskzhrdhxfWlrK9rJIcs5j9Dh1yc/jtC+u5tHgQP5w+ho55kYlzLZm0JxImLfchkoRYfRL3v77l8PtYiQKgui7Sgf3Se6X8bdXOw+XTxxdxy4yxFBUWqI9CMoJqFiJJiNUn0ZROeTlU19ZRP4Vie/nBRkuUa+6EZBLVLETimL+8hClzFjHsxqdi9i/Es2nO+fTq2pGGc+1aukS5SDpQzUIkhvnLSxptVZqMoqCDujWXKBdJB6pZiMTw/SdXNztRRHdQt+YS5SLpQMlCJIayyupmnd+jc/4RHdSzp46iIP/IJcM12kkymZqhJGt9b/4qHlq8lVp3cs343ClFnDGyL8+v3dXk53556biEy3o0d4lykXRnHmfIXyYrLi72JUuWhB2GpLHvzV/F/a9viXmssHM+ZQfi1yw2aYa1tFNmttTdi2MdUzOUZJWd5Qd5bOk2HoiTKHKApd/7dGqDEskAaoaSdulQTR0f7KuitKKK7WWVLN64h5feK+X90v1Nfq4OtOucSAxKFpKx9u4/xLIte1m+pYySskp2VxyktCKSIPY2aEYqyM9l0nE9uXziYKYM7835v3qJuhjXrN9XonN+DgeqG5/ROV+VcclOShaSEerqnPWl+1i6eS9LN+9l2Za9bAhqCbk5xoDunejbrSOd8nI5FOwRUViQz7jBhawu+YjSfVW8t2sf08cV8bEBx3DFaYNj9llcPimydcr/zjiJ6/+8gujRszkWKRfJRkoWkpYqD9WyYmsZSzbtYUmQHCoO1gDQpWMutcH06K4dcsnLNUr2VrK/qoZ9B2sOz48oq6zmxXWlh69Zv8cEwI+njwU4YjTU5ZMGHS7XaCaRI2k0lKSF3RUHWbppL0s2R35Wl5Qf3hxoVL9uTBjSg1OG9GDv/kP84tl1HKyJ1YiUnKLCAl658ezWCl2k3WhqNJRqFlFWbi3jwjtib0ojqdW1Yx5XTBzM184aTvfO+YfLp8xZdFSJArTkhkhLKFlE6XdMJ75xzoiww8hKmz/czzOrdlJVG0kE+6pq+NPrmzlh4DFHNP20xhe9ltwQaT4liyj9u3fi+k+PDDuMrDRlzqLDiaJe9Lak9eLtMJcsLbkh0jIaByhpIdlVWmOtuRQtP9coLMg/vKHQlacN1gZDIq1ANQtJC8nuSd1wlFL3gnzMoOxAtUYsibQhJQs5ag33pm7JF3Zz9qTWDnMiqadkIUelfm/q+i/56LkMzflC17wGkfSmZCExJVtbiLU3dayO6WSoxiCSvpQspJHm1Ba0fahIdtBoKGmkqdpCQ9o+VCQ7KFlII82pLWj7UJHsoGQhjTSntjB9fBG3zBiruQwi7Zz6LKSR5gxjBXVMi2SDjKlZmNk0M1tnZuvN7Maw42nPVFsQkYYyomZhZrnAHcCngW3Am2b2pLu/E25k7ZdqCyISLVNqFhOB9e6+wd0PAQ8DF4Yck4hI1siUZFEEbI16vy0oO8zMZpnZEjNbUlpaioiItJ5MSRYJuftd7l7s7sV9+vQJOxwRkXYlU5JFCTAo6v2xQZmIiKRApiSLN4ERZjbMzDoAlwFPhhyTiEjWMHcPO4akmNl5wC+BXOAed/9JE+eWApuP4na9gQ+O4vNtSbG1jGJrGcXWMpka2xB3j9mOnzHJIpXMbIm7F4cdRyyKrWUUW8sotpZpj7FlSjOUiIiESMlCREQSUrKI7a6wA2iCYmsZxdYyiq1l2l1s6rMQEZGEVLMQEZGElCxERCQhJYsoZrbJzFaZ2QozWxJyLPeY2W4zezuqrKeZPWdm7wW/e6RRbN83s5Lg2a0I5sWEEdsgM3vBzN4xs9Vmdm1QHvqzayK20J+dmXUyszfMbGUQ2w+C8mFmtjjYGuCRYFJsusR2n5ltjHpu41IdW1SMuWa23MwWBO9Df25NxNai56Zk0dhZ7j4uDcZI3wdMa1B2I/C8u48Ang/eh+E+GscGcHvw7Ma5+9MpjqleDfAtdz8BOA34mpmdQHo8u3ixQfjPrgo4291PBsYB08zsNOCnQWzDgb3ANWkUG8DsqOe2IoTY6l0LrIl6nw7PrV7D2KAFz03JIk25+z+BPQ2KLwTmBq/nAtNTGVO9OLGlBXff4e7LgtcVRP6RFJEGz66J2ELnEfuCt/nBjwNnA48F5WE9t3ixpQUzOxY4H/hD8N5Ig+cWK7ajoWRxJAeeNbOlZjYr7GBi6OfuO4LXO4F+YQYTw9fN7K2gmSqUJrJoZjYUGA8sJs2eXYPYIA2eXdBcsQLYDTwHvA+UuXtNcEqjrQHCis3d65/bT4LndruZdQwjNiLLEH0HqAve9yJNnhuNY6vX7OemZHGkj7v7BOAzRJoIPhl2QPF4ZMxz2vx1BdwJHE+kmWAH8IswgzGzrsBfgG+6+0fRx8J+djFiS4tn5+617j6OyKrOE4HRYcQRS8PYzOxE4CYiMZ4K9ARuSHVcZvZZYLe7L031vRNpIrYWPTcliyjuXhL83g08TuQfTDrZZWYDAILfu0OO5zB33xX8g64Dfk+Iz87M8ol8GT/g7vOC4rR4drFiS6dnF8RTBrwATAYKzax+++XQtwaIim1a0Kzn7l4F3Es4z20KcIGZbSKyg+fZwK9Ij+fWKDYzu7+lz03JImBmXcysW/1r4Fzg7aY/lXJPAjOD1zOBJ0KM5Qj1X8SBiwjp2QXtxXcDa9z9tqhDoT+7eLGlw7Mzsz5mVhi8LiCy3/0aIl/MFwenhfXcYsW2Nir5G5E+gZQ/N3e/yd2PdfehRLZOWOTuXyANnluc2K5s6XPLS3xK1ugHPB55fuQBD7r7M2EFY2YPAWcCvc1sG3AzMAf4s5ldQ2QJ9kvSKLYzgyF4DmwCvhxGbET+mvoisCpo4wb4Lunx7OLFdnkaPLsBwFwzyyXyR+Sf3X2Bmb0DPGxmPwaWE0l26RLbIjPrAxiwAvhKCLHFcwPhP7d4HmjJc9NyHyIikpCaoUREJCElCxERSUjJQkREElKyEBGRhJQsREQkISULkRYws32Jz2r2NcdZ1IqzFlmN9tutfR+RllCyEEkf44BQlnYXSUTJQuQomdlsM3szWJitfq+FoWa2xsx+H+zB8Gww+xgzOzU4d4WZ3Wpmbwf7HfwQuDQovzS4/Alm9qKZbTCzb4T0nyiiZCFyNMzsXGAEkfV1xgGnRC1AOQK4w93HAGXA54Lye4EvBwvj1QK4+yHgf4BHgj0GHgnOHQ1MDa5/c7C2lEjKKVmIHJ1zg5/lwDIiX+4jgmMbozaWWQoMDdY46uburwXlDya4/lPuXuXuHxBZ/DDdlqWXLKG1oUSOjgG3uPv/HVEY2a+iKqqoFihowfUbXkP/ZiUUqlmIHJ2FwJeCPSowsyIz6xvv5GCJ7QozmxQUXRZ1uALo1laBihwNJQuRo+DuzxJpSnrNzFYR2Uoz0Rf+NcDvg5VnuwDlQfkLRDq0ozu4RdKCVp0VSTEz61q/p7SZ3QgMcPdrQw5LpElq/xRJvfPN7CYi//42A1eFG45IYqpZiIhIQuqzEBGRhJQsREQkISULERFJSMlCREQSUrIQEZGE/j8bB3wjuf09fQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": {
   "authorship_tag": "ABX9TyPPmP3ScEjanUEaW0j9E5X0",
   "collapsed_sections": [
    "pLW8kdDv5asl"
   ],
   "mount_file_id": "1PMcKDATwazgu5Upg753KH9DyycHRMq2E",
   "name": "3-1 최근접 이웃 회귀.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}