ch17 sklearn.tree DecisionTreeClassifier, DecisionTreeRegressor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e2a4d0b2",
   "metadata": {},
   "source": [
    "# 17차시 의사결정나무 모델: 분류 및 회귀나무\n",
    "\n",
    "## 01 의사결정나무 모델 개요\n",
    "\n",
    "### 분류 나무(Classification Tree)\n",
    "\n",
    "- 종속변수가 명목형인 경우 사용하는 의사결정나무 모델\n",
    "- 각 노드 분류 알고리즘은 이진 분류 시 지니지수(Gini index) 기반의 CART(Classification & Regression Tree) 사용\n",
    "- 과적합 방지 및 모델 단순화를 위해 Depth, Impurity 등 관련 설정 활용"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca392147",
   "metadata": {},
   "source": [
    "## 02 주요 함수 및 메서드 소개\n",
    "\n",
    "### sklearn - DecisionTreeClassifier()\n",
    "\n",
    "- 의사결정나무의 분류나무를 수행할 때 사용하는 sklearn의 함수\n",
    "- max_depth와 random_state로 모델의 성장과 결과 고정 설정 가능\n",
    "- DecisionTreeclassifier() 함수의 fit() 메서드에 독립변수와 종속변수를 할당\n",
    "\n",
    "### sklearn - DecisionTreeRegressor()\n",
    "\n",
    "- 의사결정나무의 회귀나무를 수행할 때 사용하는 sklearn의 함수\n",
    "- max_depth와 random_state로 모델의 성장과 결과 고정 설정 가능\n",
    "- DecisionTreeRegressor() 함수의 fit() 메서드에 독립변수와 종속변수를 할당"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b92e566",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ef7e165",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"강의자료/실습파일/iris.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04cc43c6",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "      <th>is_setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species  is_setosa\n",
       "0           5.1          3.5           1.4          0.2  setosa          1\n",
       "1           4.9          3.0           1.4          0.2  setosa          1\n",
       "2           4.7          3.2           1.3          0.2  setosa          1\n",
       "3           4.6          3.1           1.5          0.2  setosa          1\n",
       "4           5.0          3.6           1.4          0.2  setosa          1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_setosa\"] = (df[\"Species\"] == \"setosa\") + 0\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4587ce3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=123)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_c = DecisionTreeClassifier(random_state = 123)\n",
    "model_c.fit(X = df.iloc[:, :4],\n",
    "           y = df[\"is_setosa\"])\n",
    "model_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54d31451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_c = model_c.predict(df.iloc[:, :4])\n",
    "pred_c[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "367b6c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.2 , 0.2 , 0.2 ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_r = DecisionTreeRegressor(random_state = 123)\n",
    "model_r.fit(X = df.iloc[:, :3],\n",
    "            y = df[\"Petal.Width\"])\n",
    "pred_r = model_r.predict(df.iloc[:, :3])\n",
    "pred_r[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8afb0d80",
   "metadata": {},
   "source": [
    "## Q1 당뇨병 발병 여부를 예측하기 위해서 의사결정나무를 사용하고자 한다. 이 대 혈당, 혈압, 임신 횟수를 기반으로 예측을 했을 때 예측 정확도는?\n",
    "1) 데이터를 학습8, 평가2의 비율로 분할하시오."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1ad13a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = pd.read_csv(\"강의자료/실습파일/diabetes.csv\")\n",
    "Q1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "423d1f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "28c590bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>3</td>\n",
       "      <td>115</td>\n",
       "      <td>66</td>\n",
       "      <td>39</td>\n",
       "      <td>140</td>\n",
       "      <td>38.1</td>\n",
       "      <td>0.150</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>3</td>\n",
       "      <td>113</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.626</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>5</td>\n",
       "      <td>158</td>\n",
       "      <td>84</td>\n",
       "      <td>41</td>\n",
       "      <td>210</td>\n",
       "      <td>39.4</td>\n",
       "      <td>0.395</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.270</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>0.198</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "318            3      115             66             39      140  38.1   \n",
       "313            3      113             50             10       85  29.5   \n",
       "195            5      158             84             41      210  39.4   \n",
       "570            3       78             70              0        0  32.5   \n",
       "226            0      101             76              0        0  35.7   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "318                     0.150   28        0  \n",
       "313                     0.626   25        0  \n",
       "195                     0.395   29        1  \n",
       "570                     0.270   39        0  \n",
       "226                     0.198   26        0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1_train, Q1_test = train_test_split(Q1, train_size = 0.8, random_state = 123)\n",
    "Q1_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f2f0981d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=123)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1_c = DecisionTreeClassifier(random_state = 123)\n",
    "Q1_c.fit(X = Q1_train[[\"Glucose\", \"BloodPressure\", \"Pregnancies\"]],\n",
    "         y = Q1_train[\"Outcome\"])\n",
    "Q1_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a86cbaee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1_pred_c = Q1_c.predict(Q1_test[[\"Glucose\", \"BloodPressure\", \"Pregnancies\"]])\n",
    "Q1_pred_c[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e8e63f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e6f88642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6298701298701299"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_true = Q1_test[\"Outcome\"],\n",
    "              y_pred = Q1_pred_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b01d71d",
   "metadata": {},
   "source": [
    "## Q2 환자의 BMI를 예측하기 위하여 회귀나무를 사용하고자 한다. 이 때 혈당, 혈압, 피부 두께를 독립변수로 했을 경우 RMSE는 얼마인가?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3c0aa201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=123)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q2_r = DecisionTreeRegressor(random_state = 123)\n",
    "Q2_r.fit(X = Q1_train[[\"Glucose\", \"BloodPressure\", \"SkinThickness\"]],\n",
    "         y = Q1_train[\"BMI\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "83592c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3253a6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.8, 28.6, 33.8, 32.2])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q2_pred_r = Q2_r.predict(Q1_test[[\"Glucose\", \"BloodPressure\", \"SkinThickness\"]])\n",
    "Q2_pred_r[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d53e75f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.924605211306321"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_true = Q1_test[\"BMI\"],\n",
    "                  y_pred = Q2_pred_r) **0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa9d11ce",
   "metadata": {},
   "source": [
    "## Q3 분류나무의 파라미터를 바꿔가면서 성능 평가를 하려고 한다. 당뇨 발병 여부를 종속변수로 하고 혈당, 혈압, 임신 횟수, BMI, 나이를 독립변수로 하고 Depth를 3에서 6까지 변화시킬 때 그 결과로 틀린 것은?\n",
    "1) 데이터를 학습7, 평가3의 비율로 분할하시오.  \n",
    "2) Seed는 345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c5a006b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>196</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.444</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>80</td>\n",
       "      <td>31</td>\n",
       "      <td>53</td>\n",
       "      <td>45.2</td>\n",
       "      <td>0.089</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0.304</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>5</td>\n",
       "      <td>189</td>\n",
       "      <td>64</td>\n",
       "      <td>33</td>\n",
       "      <td>325</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.583</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "390            1      100             66             29      196  32.0   \n",
       "229            0      117             80             31       53  45.2   \n",
       "703            2      129              0              0        0  38.5   \n",
       "360            5      189             64             33      325  31.2   \n",
       "599            1      109             38             18      120  23.1   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "390                     0.444   42        0  \n",
       "229                     0.089   24        0  \n",
       "703                     0.304   41        0  \n",
       "360                     0.583   29        1  \n",
       "599                     0.407   26        0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q3_train, Q3_test = train_test_split(Q1, train_size = 0.7, random_state = 345)\n",
    "Q3_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3273fe96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 0.77\n",
      "4 0.76\n",
      "5 0.76\n",
      "6 0.77\n"
     ]
    }
   ],
   "source": [
    "for i in [3, 4, 5, 6]:\n",
    "    Q3_c = DecisionTreeClassifier(max_depth = i, random_state = 123)\n",
    "    Q3_c.fit(X = Q3_train[[\"Glucose\", \"BloodPressure\", \"Pregnancies\", \"BMI\", \"Age\"]],\n",
    "         y = Q3_train[\"Outcome\"])\n",
    "    Q3_pred_c = Q3_c.predict(Q3_test[[\"Glucose\", \"BloodPressure\", \"Pregnancies\", \"BMI\", \"Age\"]])\n",
    "    result = accuracy_score(y_true = Q3_test[\"Outcome\"],\n",
    "              y_pred = Q3_pred_c)\n",
    "    print(i, result.round(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "56f08e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>depth</th>\n",
       "      <th>accs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   depth  accs\n",
       "0      3  0.77\n",
       "1      4  0.76\n",
       "2      5  0.76\n",
       "3      6  0.77"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 답안\n",
    "cols = [\"Glucose\", \"BloodPressure\", \"Pregnancies\", \"BMI\", \"Age\"]\n",
    "depth_list = [3, 4, 5, 6]\n",
    "accs = []\n",
    "for depth in depth_list:\n",
    "    model = DecisionTreeClassifier(max_depth = depth, random_state = 345)\n",
    "    model.fit(X = Q3_train.loc[:, cols],\n",
    "             y = Q3_train[\"Outcome\"])\n",
    "    pred = model.predict(Q3_test.loc[:, cols])\n",
    "    accs = accs + [accuracy_score(y_pred = pred,\n",
    "                                 y_true = Q3_test[\"Outcome\"])]\n",
    "    \n",
    "df_score = pd.DataFrame({\"depth\": depth_list,\n",
    "                        \"accs\": accs})\n",
    "df_score[\"accs\"] = df_score[\"accs\"].round(2)\n",
    "df_score"
   ]
  }
 ],
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