训练决策树分类器

训练决策树分类器

# 训练决策树分类器
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets

#加载数据
iris = datasets.load_iris()
features = iris.data
target = iris.target
print(target)
# 创建决策树分类器
decisiontree = DecisionTreeClassifier(random_state=0)

# 训练拟合    默认是 基尼系数
model = decisiontree.fit(features, target)


[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 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
 2 2]
预测
# 样本
observation = [[ 5,  4,  3,  2]]

# 预测
model.predict(observation)
array([1])
查看属于各个类的概率
# 查看属于各个类的概率
model.predict_proba(observation)
array([[0., 1., 0.]])
训练
# 改变分类方式   使用entropy
decisiontree_entropy = DecisionTreeClassifier(
    criterion='entropy', random_state=0)

# 训练
model_entropy = decisiontree_entropy.fit(features, target)