第四章决策树实验

第四章决策树实验

核心：sklearn库使用！

1.基于ID3算法的决策树（解决葡萄酒分类问题）

信息增益

from sklearn.tree import DecisionTreeClassifier #导入决策树
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_wine
wine = load_wine()
x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, random_state=0,test_size=0.2)
clf = DecisionTreeClassifier(criterion='entropy')#基于ID3的决策树 创建决策树分类器
clf = clf.fit(x_train, y_train)#训练分类器
test_score = clf.score(x_test, y_test)#计算测试集准确率 
print(test_score)
train_score = clf.score(x_train, y_train)#计算训练集准确率
print(train_score)

2.基于CART算法的决策树（解决葡萄酒分类问题）

gini指数

from sklearn.tree import DecisionTreeClassifier #导入决策树
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_wine
wine = load_wine()
x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, random_state=0,test_size=0.2)
clf = DecisionTreeClassifier(criterion='gini')#基于cart的决策树
clf = clf.fit(x_train, y_train)
test_score = clf.score(x_test, y_test)
print(test_score)
train_score = clf.score(x_train, y_train)
print(train_score)