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  • 决策树

             决策树(红酒数据集)

    In [ ]:
    from sklearn import tree  # 导入树模块
    from sklearn.datasets import load_wine # 导入红酒数据集
    from sklearn.model_selection import train_test_split  # 导入分训练集和测试集的模块
    
    In [3]:
    wine = load_wine()
    
    In [4]:
    wine
    
    Out[4]:
    {'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,
             1.065e+03],
            [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,
             1.050e+03],
            [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,
             1.185e+03],
            ...,
            [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,
             8.350e+02],
            [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,
             8.400e+02],
            [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,
             5.600e+02]]),
     'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
            2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
            2, 2]),
     'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),
     'DESCR': 'Wine Data Database
    ====================
    
    Notes
    -----
    Data Set Characteristics:
        :Number of Instances: 178 (50 in each of three classes)
        :Number of Attributes: 13 numeric, predictive attributes and the class
        :Attribute Information:
     		- 1) Alcohol
     		- 2) Malic acid
     		- 3) Ash
    		- 4) Alcalinity of ash  
     		- 5) Magnesium
    		- 6) Total phenols
     		- 7) Flavanoids
     		- 8) Nonflavanoid phenols
     		- 9) Proanthocyanins
    		- 10)Color intensity
     		- 11)Hue
     		- 12)OD280/OD315 of diluted wines
     		- 13)Proline
            	- class:
                    - class_0
                    - class_1
                    - class_2
    		
        :Summary Statistics:
        
        ============================= ==== ===== ======= =====
                                       Min   Max   Mean     SD
        ============================= ==== ===== ======= =====
        Alcohol:                      11.0  14.8    13.0   0.8
        Malic Acid:                   0.74  5.80    2.34  1.12
        Ash:                          1.36  3.23    2.36  0.27
        Alcalinity of Ash:            10.6  30.0    19.5   3.3
        Magnesium:                    70.0 162.0    99.7  14.3
        Total Phenols:                0.98  3.88    2.29  0.63
        Flavanoids:                   0.34  5.08    2.03  1.00
        Nonflavanoid Phenols:         0.13  0.66    0.36  0.12
        Proanthocyanins:              0.41  3.58    1.59  0.57
        Colour Intensity:              1.3  13.0     5.1   2.3
        Hue:                          0.48  1.71    0.96  0.23
        OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71
        Proline:                       278  1680     746   315
        ============================= ==== ===== ======= =====
    
        :Missing Attribute Values: None
        :Class Distribution: class_0 (59), class_1 (71), class_2 (48)
        :Creator: R.A. Fisher
        :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
        :Date: July, 1988
    
    This is a copy of UCI ML Wine recognition datasets.
    https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
    
    The data is the results of a chemical analysis of wines grown in the same
    region in Italy by three different cultivators. There are thirteen different
    measurements taken for different constituents found in the three types of
    wine.
    
    Original Owners: 
    
    Forina, M. et al, PARVUS - 
    An Extendible Package for Data Exploration, Classification and Correlation. 
    Institute of Pharmaceutical and Food Analysis and Technologies,
    Via Brigata Salerno, 16147 Genoa, Italy.
    
    Citation:
    
    Lichman, M. (2013). UCI Machine Learning Repository
    [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
    School of Information and Computer Science. 
    
    References
    ----------
    (1) 
    S. Aeberhard, D. Coomans and O. de Vel, 
    Comparison of Classifiers in High Dimensional Settings, 
    Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of 
    Mathematics and Statistics, James Cook University of North Queensland. 
    (Also submitted to Technometrics). 
    
    The data was used with many others for comparing various 
    classifiers. The classes are separable, though only RDA 
    has achieved 100% correct classification. 
    (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) 
    (All results using the leave-one-out technique) 
    
    (2) 
    S. Aeberhard, D. Coomans and O. de Vel, 
    "THE CLASSIFICATION PERFORMANCE OF RDA" 
    Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of 
    Mathematics and Statistics, James Cook University of North Queensland. 
    (Also submitted to Journal of Chemometrics). 
    ',
     'feature_names': ['alcohol',
      'malic_acid',
      'ash',
      'alcalinity_of_ash',
      'magnesium',
      'total_phenols',
      'flavanoids',
      'nonflavanoid_phenols',
      'proanthocyanins',
      'color_intensity',
      'hue',
      'od280/od315_of_diluted_wines',
      'proline']}
    In [5]:
    wine.data  # 我们所需要的数据
    
    Out[5]:
    array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,
            1.065e+03],
           [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,
            1.050e+03],
           [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,
            1.185e+03],
           ...,
           [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,
            8.350e+02],
           [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,
            8.400e+02],
           [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,
            5.600e+02]])
    In [6]:
    wine.target  # 数据集的标签,三分类
    
    Out[6]:
    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
           2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
           2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
           2, 2])
    In [7]:
    import pandas as pd
    pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1) # 因为是纵向的连接,所以是axis=1,讲数据集数据和数据集标签以表的形式连接
    
    Out[7]:
     
     01234567891011120
    0 14.23 1.71 2.43 15.6 127.0 2.80 3.06 0.28 2.29 5.640000 1.04 3.92 1065.0 0
    1 13.20 1.78 2.14 11.2 100.0 2.65 2.76 0.26 1.28 4.380000 1.05 3.40 1050.0 0
    2 13.16 2.36 2.67 18.6 101.0 2.80 3.24 0.30 2.81 5.680000 1.03 3.17 1185.0 0
    3 14.37 1.95 2.50 16.8 113.0 3.85 3.49 0.24 2.18 7.800000 0.86 3.45 1480.0 0
    4 13.24 2.59 2.87 21.0 118.0 2.80 2.69 0.39 1.82 4.320000 1.04 2.93 735.0 0
    5 14.20 1.76 2.45 15.2 112.0 3.27 3.39 0.34 1.97 6.750000 1.05 2.85 1450.0 0
    6 14.39 1.87 2.45 14.6 96.0 2.50 2.52 0.30 1.98 5.250000 1.02 3.58 1290.0 0
    7 14.06 2.15 2.61 17.6 121.0 2.60 2.51 0.31 1.25 5.050000 1.06 3.58 1295.0 0
    8 14.83 1.64 2.17 14.0 97.0 2.80 2.98 0.29 1.98 5.200000 1.08 2.85 1045.0 0
    9 13.86 1.35 2.27 16.0 98.0 2.98 3.15 0.22 1.85 7.220000 1.01 3.55 1045.0 0
    10 14.10 2.16 2.30 18.0 105.0 2.95 3.32 0.22 2.38 5.750000 1.25 3.17 1510.0 0
    11 14.12 1.48 2.32 16.8 95.0 2.20 2.43 0.26 1.57 5.000000 1.17 2.82 1280.0 0
    12 13.75 1.73 2.41 16.0 89.0 2.60 2.76 0.29 1.81 5.600000 1.15 2.90 1320.0 0
    13 14.75 1.73 2.39 11.4 91.0 3.10 3.69 0.43 2.81 5.400000 1.25 2.73 1150.0 0
    14 14.38 1.87 2.38 12.0 102.0 3.30 3.64 0.29 2.96 7.500000 1.20 3.00 1547.0 0
    15 13.63 1.81 2.70 17.2 112.0 2.85 2.91 0.30 1.46 7.300000 1.28 2.88 1310.0 0
    16 14.30 1.92 2.72 20.0 120.0 2.80 3.14 0.33 1.97 6.200000 1.07 2.65 1280.0 0
    17 13.83 1.57 2.62 20.0 115.0 2.95 3.40 0.40 1.72 6.600000 1.13 2.57 1130.0 0
    18 14.19 1.59 2.48 16.5 108.0 3.30 3.93 0.32 1.86 8.700000 1.23 2.82 1680.0 0
    19 13.64 3.10 2.56 15.2 116.0 2.70 3.03 0.17 1.66 5.100000 0.96 3.36 845.0 0
    20 14.06 1.63 2.28 16.0 126.0 3.00 3.17 0.24 2.10 5.650000 1.09 3.71 780.0 0
    21 12.93 3.80 2.65 18.6 102.0 2.41 2.41 0.25 1.98 4.500000 1.03 3.52 770.0 0
    22 13.71 1.86 2.36 16.6 101.0 2.61 2.88 0.27 1.69 3.800000 1.11 4.00 1035.0 0
    23 12.85 1.60 2.52 17.8 95.0 2.48 2.37 0.26 1.46 3.930000 1.09 3.63 1015.0 0
    24 13.50 1.81 2.61 20.0 96.0 2.53 2.61 0.28 1.66 3.520000 1.12 3.82 845.0 0
    25 13.05 2.05 3.22 25.0 124.0 2.63 2.68 0.47 1.92 3.580000 1.13 3.20 830.0 0
    26 13.39 1.77 2.62 16.1 93.0 2.85 2.94 0.34 1.45 4.800000 0.92 3.22 1195.0 0
    27 13.30 1.72 2.14 17.0 94.0 2.40 2.19 0.27 1.35 3.950000 1.02 2.77 1285.0 0
    28 13.87 1.90 2.80 19.4 107.0 2.95 2.97 0.37 1.76 4.500000 1.25 3.40 915.0 0
    29 14.02 1.68 2.21 16.0 96.0 2.65 2.33 0.26 1.98 4.700000 1.04 3.59 1035.0 0
    ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
    148 13.32 3.24 2.38 21.5 92.0 1.93 0.76 0.45 1.25 8.420000 0.55 1.62 650.0 2
    149 13.08 3.90 2.36 21.5 113.0 1.41 1.39 0.34 1.14 9.400000 0.57 1.33 550.0 2
    150 13.50 3.12 2.62 24.0 123.0 1.40 1.57 0.22 1.25 8.600000 0.59 1.30 500.0 2
    151 12.79 2.67 2.48 22.0 112.0 1.48 1.36 0.24 1.26 10.800000 0.48 1.47 480.0 2
    152 13.11 1.90 2.75 25.5 116.0 2.20 1.28 0.26 1.56 7.100000 0.61 1.33 425.0 2
    153 13.23 3.30 2.28 18.5 98.0 1.80 0.83 0.61 1.87 10.520000 0.56 1.51 675.0 2
    154 12.58 1.29 2.10 20.0 103.0 1.48 0.58 0.53 1.40 7.600000 0.58 1.55 640.0 2
    155 13.17 5.19 2.32 22.0 93.0 1.74 0.63 0.61 1.55 7.900000 0.60 1.48 725.0 2
    156 13.84 4.12 2.38 19.5 89.0 1.80 0.83 0.48 1.56 9.010000 0.57 1.64 480.0 2
    157 12.45 3.03 2.64 27.0 97.0 1.90 0.58 0.63 1.14 7.500000 0.67 1.73 880.0 2
    158 14.34 1.68 2.70 25.0 98.0 2.80 1.31 0.53 2.70 13.000000 0.57 1.96 660.0 2
    159 13.48 1.67 2.64 22.5 89.0 2.60 1.10 0.52 2.29 11.750000 0.57 1.78 620.0 2
    160 12.36 3.83 2.38 21.0 88.0 2.30 0.92 0.50 1.04 7.650000 0.56 1.58 520.0 2
    161 13.69 3.26 2.54 20.0 107.0 1.83 0.56 0.50 0.80 5.880000 0.96 1.82 680.0 2
    162 12.85 3.27 2.58 22.0 106.0 1.65 0.60 0.60 0.96 5.580000 0.87 2.11 570.0 2
    163 12.96 3.45 2.35 18.5 106.0 1.39 0.70 0.40 0.94 5.280000 0.68 1.75 675.0 2
    164 13.78 2.76 2.30 22.0 90.0 1.35 0.68 0.41 1.03 9.580000 0.70 1.68 615.0 2
    165 13.73 4.36 2.26 22.5 88.0 1.28 0.47 0.52 1.15 6.620000 0.78 1.75 520.0 2
    166 13.45 3.70 2.60 23.0 111.0 1.70 0.92 0.43 1.46 10.680000 0.85 1.56 695.0 2
    167 12.82 3.37 2.30 19.5 88.0 1.48 0.66 0.40 0.97 10.260000 0.72 1.75 685.0 2
    168 13.58 2.58 2.69 24.5 105.0 1.55 0.84 0.39 1.54 8.660000 0.74 1.80 750.0 2
    169 13.40 4.60 2.86 25.0 112.0 1.98 0.96 0.27 1.11 8.500000 0.67 1.92 630.0 2
    170 12.20 3.03 2.32 19.0 96.0 1.25 0.49 0.40 0.73 5.500000 0.66 1.83 510.0 2
    171 12.77 2.39 2.28 19.5 86.0 1.39 0.51 0.48 0.64 9.899999 0.57 1.63 470.0 2
    172 14.16 2.51 2.48 20.0 91.0 1.68 0.70 0.44 1.24 9.700000 0.62 1.71 660.0 2
    173 13.71 5.65 2.45 20.5 95.0 1.68 0.61 0.52 1.06 7.700000 0.64 1.74 740.0 2
    174 13.40 3.91 2.48 23.0 102.0 1.80 0.75 0.43 1.41 7.300000 0.70 1.56 750.0 2
    175 13.27 4.28 2.26 20.0 120.0 1.59 0.69 0.43 1.35 10.200000 0.59 1.56 835.0 2
    176 13.17 2.59 2.37 20.0 120.0 1.65 0.68 0.53 1.46 9.300000 0.60 1.62 840.0 2
    177 14.13 4.10 2.74 24.5 96.0 2.05 0.76 0.56 1.35 9.200000 0.61 1.60 560.0 2

    178 rows × 14 columns

    In [8]:
    wine.feature_names # 特征名字
    
    Out[8]:
    ['alcohol',
     'malic_acid',
     'ash',
     'alcalinity_of_ash',
     'magnesium',
     'total_phenols',
     'flavanoids',
     'nonflavanoid_phenols',
     'proanthocyanins',
     'color_intensity',
     'hue',
     'od280/od315_of_diluted_wines',
     'proline']
    In [9]:
    wine.target_names
    
    Out[9]:
    array(['class_0', 'class_1', 'class_2'], dtype='<U7')
    In [10]:
    Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)  # 对数据集以7:3的比例进行进行切分
    
    In [11]:
    Xtrain.shape  # 训练集的数据大小
    
    Out[11]:
    (124, 13)
    In [12]:
    wine.data.shape  # 全部数据集的数据大小:178行,13个标签
    
    Out[12]:
    (178, 13)
    In [13]:
    clf = tree.DecisionTreeClassifier(criterion="entropy")  # 实例化
    clf = clf.fit(Xtrain, Ytrain)  # 训练模型
    score = clf.score(Xtest, Ytest) # 执行模型,得出精确度
    
    In [14]:
    score
    
    Out[14]:
    0.8333333333333334
    In [17]:
    feature_name = ['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜 色强度','色调','od280/od315稀释葡萄酒','脯氨酸']
    import graphviz
    dot_data = tree.export_graphviz(clf
                                   ,feature_names = feature_name
                                   ,class_names=["琴酒","雪莉","贝尔摩德"]
                                   ,filled=True
                                   ,rounded=True
                                   ,out_file=None
                                   )
    graph = graphviz.Source(dot_data)
    graph
    
    Out[17]:
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  • 原文地址:https://www.cnblogs.com/flypig666/p/11991424.html
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