决策树

# 导入相应模块和数据集
from sklearn import tree
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
# 加载数据
wine = load_wine()

数据信息

wine.data.shape
#(178, 13)
wine.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])
'''
import pandas as pd
pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1)

显示数据

划分数据集

# 划分训练集和测试集
Xtrain,Xtest,Ytrain,Ytest = train_test_split(wine.data,wine.target,test_size=0.3)
Xtrain.shape
Xtest.shape
#(54, 13)

决策树创建

# 创建决策树模型      random_state=30类似于确定随机数种子，保证生成的决策树不变
clf = tree.DecisionTreeClassifier(criterion='entropy',random_state=30)
clf.fit(Xtrain,Ytrain)
score = clf.score(Xtest,Ytest)
score
# 0.9629629629629629

可视化决策树

# 画出决策树
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'
               ,'od/280od315稀释葡萄酒','脯氨酸']
import graphviz
dot_data = tree.export_graphviz(clf
                               ,feature_names=feature_name
                               ,filled=True # 不同颜色
                               ,rounded=True)# 圆角
graph = graphviz.Source(dot_data)
graph

可视化结果

分析重要信息

# 重要特征
clf.feature_importances_
[*zip(feature_name,clf.feature_importances_)]

[('酒精', 0.0),
 ('苹果酸', 0.0),
 ('灰', 0.0),
 ('灰的碱性', 0.0),
 ('镁', 0.0),
 ('总酚', 0.0),
 ('类黄酮', 0.3990915395984506),
 ('非黄烷类酚类', 0.0),
 ('花青素', 0.0),
 ('颜色强度', 0.4679552572083532),
 ('色调', 0.034102883482108784),
 ('od/280od315稀释葡萄酒', 0.0),
 ('脯氨酸', 0.0988503197110873)]

决策树的裁剪

# 决策树的裁剪 防止过拟合
# 常见使用前剪枝
# max_depth 限制树的深度
# min_samples_leaf 保证节点分支后至少有多少个样本
# min_samples_split 保证节点有多少样本时才允许分支
clf = tree.DecisionTreeClassifier(criterion='entropy'
                                  ,random_state=30
                                  ,max_depth=2
                                  ,min_samples_leaf=10
                                  ,min_samples_split=10)
clf = clf.fit(Xtrain,Ytrain)
score = clf.score(Xtest,Ytest)
score
# 0.7962962962962963

可视化结果

dot_data = tree.export_graphviz(clf
                               ,feature_names=feature_name
                               ,filled=True # 不同颜色
                               ,rounded=True)# 圆角
graph = graphviz.Source(dot_data)
graph

3层决策树

其它一些接口

# 一些重要属性和接口
clf.apply(Xtest) # 返回野猪节点所在索引
'''
array([4, 1, 4, 1, 1, 4, 4, 3, 4, 4, 1, 4, 4, 3, 3, 4, 4, 1, 3, 3, 1, 1,
       4, 3, 1, 3, 1, 3, 1, 1, 3, 4, 4, 3, 4, 3, 4, 1, 3, 4, 4, 4, 1, 4,
       4, 4, 4, 4, 3, 3, 4, 4, 4, 4], dtype=int64)
'''