sklearn：决策分类树_红酒数据集

from sklearn import tree
from sklearn.datasets import load_wine  # 红酒数据
from sklearn.model_selection import train_test_split

wine = load_wine()  # 导入数据
wine

{'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': '......rics).
',
 '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']}

import pandas as pd

# 将特征数据与 target拼接起来
wine_df = pd.concat([pd.DataFrame(wine.data), pd.DataFrame(wine.target)] ,axis=1)
wine_df.columns=list(wine.feature_names) + ['target']  # 将数据特征名称与数据对应
wine_df['target'] = wine_df['target'].map(dict(zip(range(3), wine.target_names)))  # 显示类别名称
wine_df

	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline	target
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	class_0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0	class_0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0	class_0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0	class_0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0	class_0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
173	13.71	5.65	2.45	20.5	95.0	1.68	0.61	0.52	1.06	7.70	0.64	1.74	740.0	class_2
174	13.40	3.91	2.48	23.0	102.0	1.80	0.75	0.43	1.41	7.30	0.70	1.56	750.0	class_2
175	13.27	4.28	2.26	20.0	120.0	1.59	0.69	0.43	1.35	10.20	0.59	1.56	835.0	class_2
176	13.17	2.59	2.37	20.0	120.0	1.65	0.68	0.53	1.46	9.30	0.60	1.62	840.0	class_2
177	14.13	4.10	2.74	24.5	96.0	2.05	0.76	0.56	1.35	9.20	0.61	1.60	560.0	class_2

178 rows × 14 columns

# 拆分数据为：训练集和测试集
X_train, X_test, Y_train, Y_test = train_test_split(wine.data, wine.target, test_size=0.3)

clf = tree.DecisionTreeClassifier(criterion="entropy")
clf = clf.fit(X_train, Y_train)
score = clf.score(X_test, Y_test)  # 返回准确度
score

0.9444444444444444

import graphviz  # 需要提前安装graphviz

dot_data = tree.export_graphviz(clf
    ,feature_names = wine.feature_names  # 特征名
    ,class_names = wine.target_names  # 标签名
    ,filled = True  # 颜色填充
    ,rounded = True  # 圆角边框
)

graph = graphviz.Source(dot_data)
graph

clf.feature_importances_  # 查看各特征的重要性，没有被使用的特征 重要性为0

array([0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.3918564 , 0.        , 0.        , 0.1160134 ,
       0.02128596, 0.        , 0.47084424])

dict(zip(wine.feature_names, clf.feature_importances_))  # 将特征名称与重要性对应

{'alcohol': 0.0,
 'malic_acid': 0.0,
 'ash': 0.0,
 'alcalinity_of_ash': 0.0,
 'magnesium': 0.0,
 'total_phenols': 0.0,
 'flavanoids': 0.26190367697120653,
 'nonflavanoid_phenols': 0.0,
 'proanthocyanins': 0.0,
 'color_intensity': 0.11601339710491781,
 'hue': 0.0,
 'od280/od315_of_diluted_wines': 0.15123868318487035,
 'proline': 0.47084424273900527}

增加决策树随机性

• 决策树的随机性在高维度的数据集中表现的会比较好
• 在低维度数据集（比如鸢尾花数据集中），随机性就表现得不够好

clf = tree.DecisionTreeClassifier(criterion="entropy"
                                  ,random_state=50  # 随机种子
                                  # splitter：默认是best，就是上面的重要性。虽然随机，但是还是选择最重要的。
                                  # random让决策树更加随机，树会更大更深
                                  ,splitter="random"  
                                 )
clf = clf.fit(X_train, Y_train)
score = clf.score(X_test, Y_test)  # 返回准确度
score

0.8888888888888888

import graphviz
dot_data = tree.export_graphviz(clf
    ,feature_names = wine.feature_names  # 特征名
    ,class_names = wine.target_names  # 标签名
    ,filled = True  # 颜色填充
    ,rounded = True  # 圆角边框
)

graph = graphviz.Source(dot_data)
graph

剪枝参数：min_samples_leaf & min_samples_split

• 为了使决策树具有更大的泛化能力
• 限制树的最大深度，建议从3开始逐渐尝试
• 限制叶子节点数量
• 限制划分节点数量

import graphviz

clf = tree.DecisionTreeClassifier(criterion="entropy"
                                  ,random_state=50
                                  ,splitter="random"
                                  ,max_depth = 3
                                  ,min_samples_leaf=10  # 将样本数量小于10的叶子节点剪掉
                                  ,min_samples_split=10  # 将中间节点样本数量小于10的剪掉
                                 )
clf = clf.fit(X_train, Y_train)
score = clf.score(X_test, Y_test)  # 返回准确度
print(score)
dot_data = tree.export_graphviz(clf
    ,feature_names = wine.feature_names  # 特征名
    ,class_names = wine.target_names  # 标签名
    ,filled = True  # 颜色填充
    ,rounded = True  # 圆角边框
)

graph = graphviz.Source(dot_data)
graph

0.8518518518518519

max_features & min_impurity_decrease

• max_features：最大特征数量限制，超过限制的特征会被舍弃，是一种降维方式，使用较少
• min_impurity_decrease：限制信息增益大小，当信息增益小于这个值，就不再进行分支了

import graphviz

clf = tree.DecisionTreeClassifier(criterion="entropy"
                                  ,random_state=50
#                                   ,splitter="random"
                                  ,max_depth = 5
#                                   ,min_samples_leaf=10  # 将样本数量小于10的叶子节点剪掉
#                                   ,min_samples_split=10  # 将中间节点样本数量小于10的剪掉
#                                   ,max_features = 2
                                  ,min_impurity_decrease=0.1
                                 )
clf = clf.fit(X_train, Y_train)
score = clf.score(X_test, Y_test)  # 返回准确度
print(score)
dot_data = tree.export_graphviz(clf
    ,feature_names = wine.feature_names  # 特征名
    ,class_names = wine.target_names  # 标签名
    ,filled = True  # 颜色填充
    ,rounded = True  # 圆角边框
)

graph = graphviz.Source(dot_data)
graph

0.9444444444444444

确认最优参数，画学习曲线

import  matplotlib.pyplot as plt

deths_rt = []

for dep in range(1, 10):
    clf = tree.DecisionTreeClassifier(criterion="entropy"
                                      ,max_depth = dep
                                     )
    clf = clf.fit(X_train, Y_train)
    score = clf.score(X_test, Y_test)  # 返回准确度
    deths_rt.append(score)
    

plt.plot(range(1, 10), deths_rt)

目标权重参数

• class_weight & min_weight_fraction_leaf
• 注意：sklearn不接受一维矩阵

class_weight  # 目标类型的权重，其数据类型为dict或者列表内的dict，或者为"balanced"

min_weight_fraction_leaf  # 权重剪枝参数，搭配目标权重使用，比min_samples_leaf更偏向于主导类

其他常用接口

# 返回样本所在叶子节点的索引
clf.apply(X_test)

array([ 5,  5,  5,  4,  3,  5,  5,  5,  5, 10, 10,  5,  5,  3, 10, 10, 10,
        5,  4,  5, 10,  4,  5, 10,  5,  5,  4,  5,  4,  4,  5,  4,  4, 10,
       10,  5,  4,  5,  5,  5,  4, 10, 10, 10,  5,  5, 10,  4, 10, 10,  5,
        5,  5, 10], dtype=int64)

# 返回预测标签
clf.predict(X_test

array([1, 1, 1, 2, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 2, 1, 0, 2,
       1, 0, 1, 1, 2, 1, 2, 2, 1, 2, 2, 0, 0, 1, 2, 1, 1, 1, 2, 0, 0, 0,
       1, 1, 0, 2, 0, 0, 1, 1, 1, 0])