python决策树

一、CART算法的实现

#encoding:utf-8
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_digits
#准备数据
digit = load_digits()
data  = digit.data
target = digit.target
#随机抽取33%的数据做测试集，其余为训练集
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.33,random_state=0)
#创建CART分类树
clf = DecisionTreeClassifier(criterion=‘gini‘)
#拟合构造CART分类树
clf = clf.fit(train_data,train_target)
#用CART分类树做预测
test_predict = clf.predict(test_data)
#将结果输出
print(‘实际结果为：‘,test_target,‘--预测结果为：‘,test_predict)
#预测结果的准确率
score = accuracy_score(test_target,test_predict)
print("CART分类树准确率%.4f" % score)

结果：

实际结果为： [2 8 2 6 6 7 1 9 8 5 2 8 6 6 6 6 1 0 5 8 8 7 8 4 7 5 4 9 2 9 4 7 6 8 9 4 3
1 8 6 7 7 1 0 7 6 2 1 9 6 7 9 0 0 5 1 6 3 0 2 3 4 1 9 2 6 9 1 8 3 5 1
2 2 9 7 2 3 6 0 5 3 7 5 1 2 9 9 3 1 7 7 4 8 5 8 5 5 2 5 9 0 7 1 4 7 3
9 7 9 8 2 6 5 2 5 8 4 8 7 0 6 1 5 9 9 9 5 9 9 5 7 5 6 2 8 6 9 6 1 5 1
9 1 5 3 6 1 8 9 8 7 6 7 6 5 6 0 8 8 9 8 6 1 0 4 1 6 3 8 6 7 4 5 6 3 0
3 0 7 7 5 7 8 0 7 8 9 6 4 5 0 1 4 6 4 3 3 0 9 5 9 2 1 4 2 1 6 8 9 2 4
7 6 2 3 3 1 6 9 3 6 3 2 2 0 7 6 1 1 9 7 2 7 8 5 5 7 5 2 3 7 2 7 5 5 7
1 6 5 9 7 4 3 8 0 3 6 4 6 3 2 6 8 8 8 4 6 7 5 2 4 5 3 2 4 6 9 4 5 4 3
2 9 0 1 7 2 0 9 6 0 4 2 0 7 9 8 5 4 8 2 8 4 3 7 2 6 9 1 5 1 0 8 2 1 9
8 2 7 2 1 5 1 6 4 5 0 9 4 1 1 7 0 8 9 0 5 4 3 8 8 6 5 3 4 4 4 8 8 7 0
3 5 2 3 0 8 3 3 1 3 3 0 0 4 6 0 7 7 6 2 0 4 4 2 3 7 8 9 8 6 8 5 6 2 2
7 7 8 0 3 3 2 1 5 5 9 1 3 7 0 0 7 0 4 5 9 3 3 4 3 1 8 9 8 3 6 2 1 6 2
5 5 1 9 2 8 9 7 2 1 4 9 3 2 6 2 5 9 6 5 8 2 0 7 8 0 5 8 4 1 8 6 4 3 4
4 5 8 3 9 1 8 3 4 5 0 8 5 6 3 0 6 9 1 5 2 2 1 9 8 4 3 3 0 7 8 8 1 1 3
8 4 9 7 8 4 4 9 0 1 6 9 3 6 1 7 0 6 2 9 9 3 6 1 5 1 8 9 8 4 1 7 2 8 0
1 0 6 1 6 5 2 8 6 2 1 4 6 8 2 2 7 5 9 1 9 5 0 2 5 5 6 8 9 5 7 0 5 2 1
2] --预测结果为： [3 8 2 6 6 7 1 9 8 0 2 8 6 6 6 6 4 0 5 8 8 7 8 4 7 5 4 9 2 9 4 7 6 8 9 8 3
1 8 6 7 7 1 0 7 0 2 1 9 6 7 9 9 0 9 1 6 3 0 2 3 4 1 9 2 6 9 1 8 6 5 1
2 4 9 7 2 3 6 0 9 3 7 5 1 2 0 9 3 1 4 1 4 8 5 4 5 1 7 5 9 0 5 1 4 8 3
9 7 9 8 0 4 5 2 5 3 4 8 7 0 6 1 5 3 3 9 5 9 9 5 7 5 6 2 8 6 5 6 1 5 1
9 1 3 3 6 1 8 9 2 7 6 7 6 5 6 0 8 8 9 8 8 1 0 4 2 6 3 8 6 7 4 4 6 3 9
3 0 7 7 5 7 8 0 7 8 9 6 4 5 0 1 4 6 4 3 3 0 9 5 5 1 3 4 2 1 6 8 9 7 4
7 6 2 3 3 1 6 9 3 6 3 7 2 0 7 6 1 1 3 7 3 7 8 5 5 7 5 3 3 7 2 7 5 5 7
1 6 5 9 7 4 3 8 0 3 6 4 6 3 1 6 8 8 8 4 6 7 5 2 4 2 3 2 4 6 9 0 5 4 3
2 5 0 1 7 2 0 9 6 6 4 2 0 7 9 8 5 7 8 2 8 4 3 7 2 6 7 1 5 9 0 8 2 4 9
8 2 7 2 1 5 1 6 4 5 0 9 4 1 2 7 0 5 9 0 5 4 3 8 8 6 5 3 4 4 4 2 8 7 0
3 5 2 3 0 8 1 3 1 3 3 0 0 7 6 0 7 7 6 8 0 4 4 8 3 7 8 9 0 6 8 5 6 2 2
7 7 3 0 3 3 2 1 5 5 9 1 9 7 0 0 7 0 4 5 8 3 3 4 1 1 8 9 7 9 6 3 1 6 2
5 5 4 9 2 8 9 4 2 1 4 1 3 1 6 2 5 9 4 5 1 4 0 7 8 0 5 8 4 1 8 6 2 3 4
4 5 8 3 8 1 8 3 4 5 0 8 5 6 3 0 6 9 1 5 1 2 1 9 9 4 8 3 0 7 8 8 1 1 3
8 4 9 7 8 4 4 9 0 1 6 9 3 6 1 7 0 4 2 9 5 9 6 1 5 1 1 9 1 4 1 7 2 8 0
1 0 6 1 6 5 2 8 6 2 1 4 6 8 2 6 7 5 3 1 9 5 0 2 5 5 6 4 9 5 7 0 8 2 1
2]
CART分类树准确率0.8586