13.2 决策树 随机森林 泰坦尼克数据集.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f74cd788",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873bf653",
   "metadata": {},
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db9fe156",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./dataset/tt/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ea9dcaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e513308e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b893eb79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04ab57d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age'] = data['Age'].fillna(data['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9197573",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40361010",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Sex'] = [1 if x=='male' else 0 for x in data.Sex]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "38f48744",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['p1'] = np.array(data['Pclass']==1).astype(np.int32)\n",
    "data['p2'] = np.array(data['Pclass']==2).astype(np.int32)\n",
    "data['p3'] = np.array(data['Pclass']==3).astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d37e5ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['Pclass']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e2f84f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', 0], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Embarked.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fc309e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['e1'] = np.array(data['Embarked']=='S').astype(np.int32)\n",
    "data['e2'] = np.array(data['Embarked']=='C').astype(np.int32)\n",
    "data['e3'] = np.array(data['Embarked']=='Q').astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6fac86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['Embarked']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0307f553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeb8882b",
   "metadata": {},
   "source": [
    "一般没有必要做标准化，只有需要用数据距离进行划分时，才会用到标准化"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4a1dade6",
   "metadata": {},
   "source": [
    "0-1标准化：减均值除方差\n",
    "min-max标准化：(x-xmin)/(xmax-xmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d2458f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>Survived</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>e1</th>\n",
       "      <th>e2</th>\n",
       "      <th>e3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Sex   Age  SibSp  Parch     Fare  p1  p2  p3  e1  e2  e3\n",
       "0         0    1  22.0      1      0   7.2500   0   0   1   1   0   0\n",
       "1         1    0  38.0      1      0  71.2833   1   0   0   0   1   0\n",
       "2         1    0  26.0      0      0   7.9250   0   0   1   1   0   0\n",
       "3         1    0  35.0      1      0  53.1000   1   0   0   1   0   0\n",
       "4         0    1  35.0      0      0   8.0500   0   0   1   1   0   0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d6cf5678",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data[[x for x in data.columns if x != 'Survived']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd0e095c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_target = data['Survived'].values.reshape(len(data),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ee72d8d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 11), (891, 1))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(data_train), np.shape(data_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c7677bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "# model_selection：评价模型相关工具"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2416ba84",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(data_train, data_target,\n",
    "                                                    test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed8e9500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((712, 11), (179, 11))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91d470b",
   "metadata": {},
   "source": [
    "# 决策树"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318286c7",
   "metadata": {},
   "source": [
    "如果特征为连续值，需要根据需求对数据进行离散化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a31f4b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f73986fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "112afca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7e61eae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "93a4f42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9859550561797753"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18610c11",
   "metadata": {},
   "source": [
    "## 控制树的深度处理过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e981ea7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def m_score(depth):\n",
    "    model = DecisionTreeClassifier(max_depth=depth)\n",
    "    model.fit(x_train, y_train)\n",
    "    train_score = model.score(x_train, y_train)\n",
    "    test_score = model.score(x_test, y_test)\n",
    "    return train_score, test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "03a8dc4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "depths = range(2,15)\n",
    "scores = [m_score(depth) for depth in depths]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ccb166eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.7907303370786517, 0.770949720670391),\n",
       " (0.8188202247191011, 0.8156424581005587),\n",
       " (0.8412921348314607, 0.7988826815642458),\n",
       " (0.8539325842696629, 0.8100558659217877),\n",
       " (0.8609550561797753, 0.7877094972067039),\n",
       " (0.8848314606741573, 0.8044692737430168),\n",
       " (0.9030898876404494, 0.7653631284916201),\n",
       " (0.9171348314606742, 0.7932960893854749),\n",
       " (0.9325842696629213, 0.7877094972067039),\n",
       " (0.9480337078651685, 0.776536312849162),\n",
       " (0.9662921348314607, 0.8100558659217877),\n",
       " (0.9719101123595506, 0.8100558659217877),\n",
       " (0.976123595505618, 0.8044692737430168)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e9e944a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_s = [s[0] for s in scores]\n",
    "test_s = [s[1] for s in scores]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "098be7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a775ae1690>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_s)\n",
    "plt.plot(test_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77cb137",
   "metadata": {},
   "source": [
    "## 设置信息增益阈值处理过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "30161fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "def m_score(depth):\n",
    "    model = DecisionTreeClassifier(min_impurity_split=depth)\n",
    "    model.fit(x_train, y_train)\n",
    "    train_score = model.score(x_train, y_train)\n",
    "    test_score = model.score(x_test, y_test)\n",
    "    return train_score, test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f1578923",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = np.linspace(0,0.5,50)\n",
    "scores = [m_score(value) for value in values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "40aa91bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_s = [s[0] for s in scores]\n",
    "test_s = [s[1] for s in scores]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ade2f511",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_index = np.argmax(test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "71352a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_score = test_s[best_index]\n",
    "best_value = values[best_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6a12f465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8156424581005587, 0.01020408163265306)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_score, best_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "83b51838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a775bb17b0>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_s)\n",
    "plt.plot(test_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1287b864",
   "metadata": {},
   "source": [
    "# 交叉验证"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d857892",
   "metadata": {},
   "source": [
    "为什么需要交叉验证——解决随机划分的差异和参数选择的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c031eb7",
   "metadata": {},
   "source": [
    "让所有的数据都参加到训练和评价当中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "04429fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ac3585b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = np.linspace(0, 0.5, 50)\n",
    "depths = range(2,15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1d651602",
   "metadata": {},
   "outputs": [],
   "source": [
    "# param_grid = {'max_depth':depths, 'min_impurity_split': values}\n",
    "param_grid = {'max_depth':depths, 'min_impurity_decrease': values}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2d8c7f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5)  # 5折交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d1db9448",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: range(2, 15),\n",
       "                         &#x27;min_impurity_decrease&#x27;: array([0.        , 0.01020408, 0.02040816, 0.03061224, 0.04081633,\n",
       "       0.05102041, 0.06122449, 0.07142857, 0.08163265, 0.09183673,\n",
       "       0.10204082, 0.1122449 , 0.12244898, 0.13265306, 0.14285714,\n",
       "       0.15306122, 0.16326531, 0.17346939, 0.18367347, 0.19387755,\n",
       "       0.20408163, 0.21428571, 0.2244898 , 0.23469388, 0.24489796,\n",
       "       0.25510204, 0.26530612, 0.2755102 , 0.28571429, 0.29591837,\n",
       "       0.30612245, 0.31632653, 0.32653061, 0.33673469, 0.34693878,\n",
       "       0.35714286, 0.36734694, 0.37755102, 0.3877551 , 0.39795918,\n",
       "       0.40816327, 0.41836735, 0.42857143, 0.43877551, 0.44897959,\n",
       "       0.45918367, 0.46938776, 0.47959184, 0.48979592, 0.5       ])})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: range(2, 15),\n",
       "                         &#x27;min_impurity_decrease&#x27;: array([0.        , 0.01020408, 0.02040816, 0.03061224, 0.04081633,\n",
       "       0.05102041, 0.06122449, 0.07142857, 0.08163265, 0.09183673,\n",
       "       0.10204082, 0.1122449 , 0.12244898, 0.13265306, 0.14285714,\n",
       "       0.15306122, 0.16326531, 0.17346939, 0.18367347, 0.19387755,\n",
       "       0.20408163, 0.21428571, 0.2244898 , 0.23469388, 0.24489796,\n",
       "       0.25510204, 0.26530612, 0.2755102 , 0.28571429, 0.29591837,\n",
       "       0.30612245, 0.31632653, 0.32653061, 0.33673469, 0.34693878,\n",
       "       0.35714286, 0.36734694, 0.37755102, 0.3877551 , 0.39795918,\n",
       "       0.40816327, 0.41836735, 0.42857143, 0.43877551, 0.44897959,\n",
       "       0.45918367, 0.46938776, 0.47959184, 0.48979592, 0.5       ])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'max_depth': range(2, 15),\n",
       "                         'min_impurity_decrease': array([0.        , 0.01020408, 0.02040816, 0.03061224, 0.04081633,\n",
       "       0.05102041, 0.06122449, 0.07142857, 0.08163265, 0.09183673,\n",
       "       0.10204082, 0.1122449 , 0.12244898, 0.13265306, 0.14285714,\n",
       "       0.15306122, 0.16326531, 0.17346939, 0.18367347, 0.19387755,\n",
       "       0.20408163, 0.21428571, 0.2244898 , 0.23469388, 0.24489796,\n",
       "       0.25510204, 0.26530612, 0.2755102 , 0.28571429, 0.29591837,\n",
       "       0.30612245, 0.31632653, 0.32653061, 0.33673469, 0.34693878,\n",
       "       0.35714286, 0.36734694, 0.37755102, 0.3877551 , 0.39795918,\n",
       "       0.40816327, 0.41836735, 0.42857143, 0.43877551, 0.44897959,\n",
       "       0.45918367, 0.46938776, 0.47959184, 0.48979592, 0.5       ])})"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data_train, data_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cec693e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 6, 'min_impurity_decrease': 0.0}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0d34f4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8170736300295023"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4615b314",
   "metadata": {},
   "source": [
    "# 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "32b8ed12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((712, 11), (179, 11))"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "35f7d91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "edf7dc4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(n_estimators=100, n_jobs=4)\n",
    "# n_estimators=100 100颗决策树，n_jobs=4，处理器为4核\n",
    "# max_features 最大特征数，min_sample_leaf，叶子的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "725b3c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f30eafb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 74,
     "metadata": {},
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    }
   ],
   "source": [
    "y_train.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "26aed715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_jobs=4)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_jobs=4)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train.ravel())  # 模型要求y必须为1列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "9c64d310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8268156424581006"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "52a43e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2605452 , 0.26374442, 0.05267658, 0.0382214 , 0.24781589,\n",
       "       0.02800777, 0.01477589, 0.05457004, 0.01605285, 0.01461969,\n",
       "       0.00897026])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_importances_  # 每个特征的重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e27e3c98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sex :  0.2605451984578339\n",
      "Age :  0.26374442405468573\n",
      "SibSp :  0.05267657561222891\n",
      "Parch :  0.038221403793938465\n",
      "Fare :  0.24781589170572155\n",
      "p1 :  0.0280077665796943\n",
      "p2 :  0.014775893932383533\n",
      "p3 :  0.054570038964384275\n",
      "e1 :  0.016052852543197944\n",
      "e2 :  0.014619692568660756\n",
      "e3 :  0.008970261787270525\n"
     ]
    }
   ],
   "source": [
    "for futh, imp in zip(['Sex','Age','SibSp','Parch','Fare','p1','p2','p3','e1',\n",
    "                       'e2','e3'], model.feature_importances_):\n",
    "    print(futh, ': ', imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c02c95c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "786dce25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DecisionTreeClassifier(max_features='sqrt', random_state=915135450),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=564334065),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=552731053),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=896758675),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=938643349),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1685303782),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1388441203),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=460216805),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=720429368),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=52647630),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1444921706),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1556603654),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=362297036),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=896436575),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=631074102),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1972901363),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1162705917),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=733976452),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=665531137),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1695665980),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=361125682),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=278576914),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1306198148),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=420478810),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=554174276),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1314690739),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1971963511),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1462136976),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1560407559),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1218828432),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=2118110669),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1107949935),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=2052335222),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1065422311),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=678099893),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1680047282),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=2053755660),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1118341467),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=396984136),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1765062417),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=554932745),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=527410980),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=224615672),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=109391147),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=161375270),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1554027550),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1582382280),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=2022003177),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1977011211),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=351551812),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=228929759),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=495287652),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=2125225544),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1116176018),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1861804598),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=2080943391),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1842461885),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1646886581),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1851600538),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=881959514),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=644600828),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1742968835),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1607041361),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=116186591),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=564869413),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1484883375),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1284419968),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=601984111),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=468427108),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1548918868),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1737046213),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=607011845),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=251960175),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1404586339),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=562611630),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=303285654),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1335565838),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=664928564),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1613971600),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=745086997),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1073033689),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1016000992),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=330276363),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=316281638),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=56329268),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1443122309),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=390006120),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1563452020),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1407530905),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1137155002),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1376417779),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=691623395),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=925972691),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1375123409),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=657846646),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=524952804),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1655444772),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=336021431),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=796518133),\n",
       " DecisionTreeClassifier(max_features='sqrt', random_state=1295169249)]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.estimators_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0ce594",
   "metadata": {},
   "source": [
    "# 交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0d5b3a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "80e935e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators = range(80, 130)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "bc9ceb54",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'n_estimators': n_estimators}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "aa10d9a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GridSearchCV(RandomForestClassifier(), param_grid, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ec1201fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;n_estimators&#x27;: range(80, 130)})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;n_estimators&#x27;: range(80, 130)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
       "             param_grid={'n_estimators': range(80, 130)})"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data_train, data_target.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "602012cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 94}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c0a38aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8193333751804657"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cc5dcbc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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