决策树-分类-红酒.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "         1.065e+03],\n",
       "        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "         1.050e+03],\n",
       "        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "         1.185e+03],\n",
       "        ...,\n",
       "        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "         8.350e+02],\n",
       "        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "         8.400e+02],\n",
       "        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "         5.600e+02]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),\n",
       " 'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n',\n",
       " 'feature_names': ['alcohol',\n",
       "  'malic_acid',\n",
       "  'ash',\n",
       "  'alcalinity_of_ash',\n",
       "  'magnesium',\n",
       "  'total_phenols',\n",
       "  'flavanoids',\n",
       "  'nonflavanoid_phenols',\n",
       "  'proanthocyanins',\n",
       "  'color_intensity',\n",
       "  'hue',\n",
       "  'od280/od315_of_diluted_wines',\n",
       "  'proline']}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       ...,\n",
       "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "        8.350e+02],\n",
       "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "        8.400e+02],\n",
       "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "        5.600e+02]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  target  \n",
       "0                            3.92   1065.0       0  \n",
       "1                            3.40   1050.0       0  \n",
       "2                            3.17   1185.0       0  \n",
       "3                            3.45   1480.0       0  \n",
       "4                            2.93    735.0       0  \n",
       "..                            ...      ...     ...  \n",
       "173                          1.74    740.0       2  \n",
       "174                          1.56    750.0       2  \n",
       "175                          1.56    835.0       2  \n",
       "176                          1.62    840.0       2  \n",
       "177                          1.60    560.0       2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# 将 wine.data 和 wine.target 转换为 DataFrame 并合并\n",
    "data_df = pd.DataFrame(wine.data, columns=wine.feature_names)\n",
    "target_df = pd.DataFrame(wine.target, columns=['target'])\n",
    "wine_df = pd.concat([data_df, target_df], axis=1)\n",
    "wine_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(wine.data,wine.target,test_size=0.3)#划分训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9444444444444444"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion='entropy',\n",
    "                                  random_state=30,\n",
    "                                  splitter='random',\n",
    "                                  max_depth=5,\n",
    "                                  )#进行决策树分类\n",
    "clf = clf.fit(xtrain, ytrain)#fit相当于一个接口，引入训练集\n",
    "score = clf.score(xtest, ytest)#测试集检测accuracy，对模型打分\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"middle\" x=\"445.12\" y=\"-251.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = class_2</text>\n",
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       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>9&#45;&gt;10</title>\n",
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       "<title>11</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M628.25,-318.75C628.25,-318.75 532,-318.75 532,-318.75 526,-318.75 520,-312.75 520,-306.75 520,-306.75 520,-256.75 520,-256.75 520,-250.75 526,-244.75 532,-244.75 532,-244.75 628.25,-244.75 628.25,-244.75 634.25,-244.75 640.25,-250.75 640.25,-256.75 640.25,-256.75 640.25,-306.75 640.25,-306.75 640.25,-312.75 634.25,-318.75 628.25,-318.75\"/>\n",
       "<text text-anchor=\"middle\" x=\"580.12\" y=\"-301.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"580.12\" y=\"-284.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 23</text>\n",
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       "<!-- 9&#45;&gt;11 -->\n",
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       "<!-- 13 -->\n",
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       "<title>13</title>\n",
       "<path fill=\"#46e789\" stroke=\"black\" d=\"M854.62,-453.5C854.62,-453.5 727.62,-453.5 727.62,-453.5 721.62,-453.5 715.62,-447.5 715.62,-441.5 715.62,-441.5 715.62,-375 715.62,-375 715.62,-369 721.62,-363 727.62,-363 727.62,-363 854.62,-363 854.62,-363 860.62,-363 866.62,-369 866.62,-375 866.62,-375 866.62,-441.5 866.62,-441.5 866.62,-447.5 860.62,-453.5 854.62,-453.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"791.12\" y=\"-436.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">color_intensity &lt;= 3.0</text>\n",
       "<text text-anchor=\"middle\" x=\"791.12\" y=\"-419.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.337</text>\n",
       "<text text-anchor=\"middle\" x=\"791.12\" y=\"-403.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
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       "<!-- 12&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>12&#45;&gt;13</title>\n",
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       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
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       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-436.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">alcalinity_of_ash &lt;= 23.578</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-419.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.359</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-403.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 44</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-386.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [41, 3, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-370.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = class_0</text>\n",
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       "<!-- 12&#45;&gt;18 -->\n",
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       "<title>12&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M856.54,-504.56C888.85,-490.14 928.64,-472.39 965.08,-456.12\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"966.17,-459.47 973.88,-452.2 963.32,-453.08 966.17,-459.47\"/>\n",
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       "<!-- 14 -->\n",
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       "<title>14</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M766.25,-318.75C766.25,-318.75 670,-318.75 670,-318.75 664,-318.75 658,-312.75 658,-306.75 658,-306.75 658,-256.75 658,-256.75 658,-250.75 664,-244.75 670,-244.75 670,-244.75 766.25,-244.75 766.25,-244.75 772.25,-244.75 778.25,-250.75 778.25,-256.75 778.25,-256.75 778.25,-306.75 778.25,-306.75 778.25,-312.75 772.25,-318.75 766.25,-318.75\"/>\n",
       "<text text-anchor=\"middle\" x=\"718.12\" y=\"-301.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"718.12\" y=\"-284.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\n",
       "<text text-anchor=\"middle\" x=\"718.12\" y=\"-268.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 11, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"718.12\" y=\"-251.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = class_1</text>\n",
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       "<!-- 13&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>13&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M764.98,-362.65C758.52,-351.65 751.6,-339.84 745.11,-328.77\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"748.14,-327.02 740.06,-320.16 742.1,-330.56 748.14,-327.02\"/>\n",
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       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<path fill=\"#6aeca0\" stroke=\"black\" d=\"M920.12,-327C920.12,-327 808.12,-327 808.12,-327 802.12,-327 796.12,-321 796.12,-315 796.12,-315 796.12,-248.5 796.12,-248.5 796.12,-242.5 802.12,-236.5 808.12,-236.5 808.12,-236.5 920.12,-236.5 920.12,-236.5 926.12,-236.5 932.12,-242.5 932.12,-248.5 932.12,-248.5 932.12,-315 932.12,-315 932.12,-321 926.12,-327 920.12,-327\"/>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-309.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">proline &lt;= 735.259</text>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-293.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.722</text>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-276.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-260.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 4, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-243.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = class_1</text>\n",
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       "<!-- 13&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>13&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M817.27,-362.65C822.1,-354.43 827.18,-345.76 832.15,-337.28\"/>\n",
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       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M777.25,-192.25C777.25,-192.25 687,-192.25 687,-192.25 681,-192.25 675,-186.25 675,-180.25 675,-180.25 675,-130.25 675,-130.25 675,-124.25 681,-118.25 687,-118.25 687,-118.25 777.25,-118.25 777.25,-118.25 783.25,-118.25 789.25,-124.25 789.25,-130.25 789.25,-130.25 789.25,-180.25 789.25,-180.25 789.25,-186.25 783.25,-192.25 777.25,-192.25\"/>\n",
       "<text text-anchor=\"middle\" x=\"732.12\" y=\"-174.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"732.12\" y=\"-158.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
       "<text text-anchor=\"middle\" x=\"732.12\" y=\"-141.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4, 0]</text>\n",
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       "<!-- 15&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>15&#45;&gt;16</title>\n",
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       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>17</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M909.25,-192.25C909.25,-192.25 819,-192.25 819,-192.25 813,-192.25 807,-186.25 807,-180.25 807,-180.25 807,-130.25 807,-130.25 807,-124.25 813,-118.25 819,-118.25 819,-118.25 909.25,-118.25 909.25,-118.25 915.25,-118.25 921.25,-124.25 921.25,-130.25 921.25,-130.25 921.25,-180.25 921.25,-180.25 921.25,-186.25 915.25,-192.25 909.25,-192.25\"/>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-174.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-158.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"middle\" x=\"864.12\" y=\"-141.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 0, 0]</text>\n",
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       "<!-- 15&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>15&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M864.12,-236.15C864.12,-225.6 864.12,-214.31 864.12,-203.63\"/>\n",
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       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"#e6843e\" stroke=\"black\" d=\"M1130.25,-327C1130.25,-327 1010,-327 1010,-327 1004,-327 998,-321 998,-315 998,-315 998,-248.5 998,-248.5 998,-242.5 1004,-236.5 1010,-236.5 1010,-236.5 1130.25,-236.5 1130.25,-236.5 1136.25,-236.5 1142.25,-242.5 1142.25,-248.5 1142.25,-248.5 1142.25,-315 1142.25,-315 1142.25,-321 1136.25,-327 1130.25,-327\"/>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-309.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">malic_acid &lt;= 4.639</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-293.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.165</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-276.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 41</text>\n",
       "<text text-anchor=\"middle\" x=\"1070.12\" y=\"-260.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [40, 1, 0]</text>\n",
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       "<!-- 18&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>18&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1070.12,-362.65C1070.12,-354.97 1070.12,-346.89 1070.12,-338.94\"/>\n",
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       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>22</title>\n",
       "<path fill=\"#9cf2c0\" stroke=\"black\" d=\"M1312,-327C1312,-327 1220.25,-327 1220.25,-327 1214.25,-327 1208.25,-321 1208.25,-315 1208.25,-315 1208.25,-248.5 1208.25,-248.5 1208.25,-242.5 1214.25,-236.5 1220.25,-236.5 1220.25,-236.5 1312,-236.5 1312,-236.5 1318,-236.5 1324,-242.5 1324,-248.5 1324,-248.5 1324,-315 1324,-315 1324,-321 1318,-327 1312,-327\"/>\n",
       "<text text-anchor=\"middle\" x=\"1266.12\" y=\"-309.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">hue &lt;= 1.221</text>\n",
       "<text text-anchor=\"middle\" x=\"1266.12\" y=\"-293.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\n",
       "<text text-anchor=\"middle\" x=\"1266.12\" y=\"-276.7\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"1266.12\" y=\"-260.2\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 2, 0]</text>\n",
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       "<!-- 18&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>18&#45;&gt;22</title>\n",
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       "<title>20</title>\n",
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       "<title>19&#45;&gt;20</title>\n",
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       "<title>21</title>\n",
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       "<text text-anchor=\"middle\" x=\"1134.12\" y=\"-158.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<title>19&#45;&gt;21</title>\n",
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       "<title>23</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1311.25,-192.25C1311.25,-192.25 1221,-192.25 1221,-192.25 1215,-192.25 1209,-186.25 1209,-180.25 1209,-180.25 1209,-130.25 1209,-130.25 1209,-124.25 1215,-118.25 1221,-118.25 1221,-118.25 1311.25,-118.25 1311.25,-118.25 1317.25,-118.25 1323.25,-124.25 1323.25,-130.25 1323.25,-130.25 1323.25,-180.25 1323.25,-180.25 1323.25,-186.25 1317.25,-192.25 1311.25,-192.25\"/>\n",
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       "<text text-anchor=\"middle\" x=\"1266.12\" y=\"-158.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "</g>\n",
       "<!-- 22&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>22&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1266.12,-236.15C1266.12,-225.6 1266.12,-214.31 1266.12,-203.63\"/>\n",
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       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node25\" class=\"node\">\n",
       "<title>24</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M1443.25,-192.25C1443.25,-192.25 1353,-192.25 1353,-192.25 1347,-192.25 1341,-186.25 1341,-180.25 1341,-180.25 1341,-130.25 1341,-130.25 1341,-124.25 1347,-118.25 1353,-118.25 1353,-118.25 1443.25,-118.25 1443.25,-118.25 1449.25,-118.25 1455.25,-124.25 1455.25,-130.25 1455.25,-130.25 1455.25,-180.25 1455.25,-180.25 1455.25,-186.25 1449.25,-192.25 1443.25,-192.25\"/>\n",
       "<text text-anchor=\"middle\" x=\"1398.12\" y=\"-174.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"1398.12\" y=\"-158.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"middle\" x=\"1398.12\" y=\"-141.95\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"1398.12\" y=\"-125.45\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = class_1</text>\n",
       "</g>\n",
       "<!-- 22&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>22&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1313.41,-236.15C1325.79,-224.48 1339.13,-211.89 1351.48,-200.25\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1353.87,-202.8 1358.74,-193.39 1349.07,-197.71 1353.87,-202.8\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x1e472d943d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz \n",
    "dot_data = tree.export_graphviz(clf,\n",
    "                              feature_names=wine.feature_names,  #特征名\n",
    "                              class_names=wine.target_names,  #类别名\n",
    "                              filled=True, rounded=True)  #填充颜色\n",
    "graph = graphviz.Source(dot_data) \n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "test=[]\n",
    "for i in range(10):\n",
    "    clf = tree.DecisionTreeClassifier(max_depth=i+1,\n",
    "                                      criterion='entropy',\n",
    "                                      random_state=30,\n",
    "                                      splitter='random'\n",
    "                                      )\n",
    "    clf.fit(xtrain,ytrain)\n",
    "    score=clf.score(xtest,ytest)\n",
    "    test.append(score)\n",
    "plt.plot(range(1,11),test,'o-')\n",
    "plt.title('max_depth')\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()\n",
    "\n",
    "#剪枝参数\n",
    "#min_samples_split: \n",
    "#min_samples_leaf: \n",
    "#min_weight_fraction_leaf: \n",
    "#max_features: \n",
    "#max_leaf_nodes: \n",
    "#min_impurity_decrease: \n",
    "#min_impurity_split: \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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