3-1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGYbZJQsfa4_"
      },
      "source": [
        "# k-최근접 이웃 회귀"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lpY20cgOfa5C"
      },
      "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": 1,
      "metadata": {
        "id": "fL3wuWxD0cH6"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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",
      "execution_count": 3,
      "metadata": {
        "id": "cc-Fn23Q4AqQ"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "gE78Nuog4Eg4",
        "outputId": "82c964a6-8dda-4776-e261-791bd3a639eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "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": {
        "id": "dqSDbM-K4pkB"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "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/"
        },
        "id": "sC6HAwnnK4aU",
        "outputId": "bf81da57-48ad-4099-804e-f0354f148c29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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/"
        },
        "id": "Og1eucsRwzIs",
        "outputId": "7574d7af-898d-4a8c-ca3a-fe777e87121b"
      },
      "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",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-DXX-xtw8Jb",
        "outputId": "9829ec91-d222-4952-bc77-c7539b2abade"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 2)\n"
          ]
        }
      ],
      "source": [
        "test_array = test_array.reshape(2, 2)\n",
        "print(test_array.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "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": {
        "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/"
        },
        "id": "0c8e0UrkLJDe",
        "outputId": "731a1d90-d7f4-470b-f73a-55c9fe3535dd"
      },
      "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": 13,
      "metadata": {
        "id": "BcPh-Da44lhx"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "oe7MTnof45sP",
        "outputId": "25d60581-39bb-4693-b7af-8bdcb95f5ad9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\">?<span>Documentation for KNeighborsRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsRegressor()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "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/"
        },
        "id": "yEv88u6LIokr",
        "outputId": "a936a881-6ee3-4d8a-93f2-5c1e89783716"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.992809406101064"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "knr.score(test_input, test_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "R8Uju0xGLX3s"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKEf3y-5KVQx",
        "outputId": "7683947b-7929-4e21-f669-31c67ae1e8cc"
      },
      "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": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoXIfmiAJaNw",
        "outputId": "9fef548f-caa2-407f-e1a6-c598d910398b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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/"
        },
        "id": "Jhu9abILLHjq",
        "outputId": "33126437-039f-4831-c9ed-9b5d5fcbb35d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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/"
        },
        "id": "cHedpJWCLVwb",
        "outputId": "cc2f4c62-ddf7-4067-a906-07b2c8071014"
      },
      "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": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ICPoeo9c2RLG",
        "outputId": "2e26b00d-3124-4f53-acf0-cdb320555ef1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "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": [
        "pLW8kdDv5asl"
      ],
      "name": "3-1 최근접 이웃 회귀.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}