Python实现CART，并且展示混淆矩阵-优快云博客

本文链接：https://blog.youkuaiyun.com/piaodexin/article/details/77853166

本文介绍了一种使用Python中的scikit-learn库实现CART决策树算法的方法，并通过鸢尾花数据集进行了分类任务。文章详细展示了从数据加载到模型训练及评估的全过程。

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# -*- coding: utf-8 -*-
"""
Created on Tue Sep  5 16:18:15 2017

@author: piaodexin
"""

from sklearn import datasets
from sklearn import cross_validation
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics  #可以展示混淆矩阵，

data=datasets.load_iris()
x=data.data
y=data.target

x_train,x_test,y_train,y_test=cross_validation.train_test_split(x,y,test_size=0.25,
                                        random_state=0,stratify=y)

#确认模型
cart=DecisionTreeClassifier()
'''
DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter='best')
'''
#训练模型
cart.fit(x_train,y_train)
cart.score(x_test,y_test)

#展示模型预测结果
print(metrics.classification_report(y_test,cart.predict(x_test))) 
print(metrics.confusion_matrix(y_test,cart.predict(x_test)))

'''
print(metrics.classification_report(y_test,cart.predict(x_test)))
             precision    recall  f1-score   support

          0       1.00      1.00      1.00        13
          1       0.93      1.00      0.96        13
          2       1.00      0.92      0.96        12

avg / total       0.98      0.97      0.97        38


print(metrics.confusion_matrix(y_test,cart.predict(x_test)))
[[13  0  0]
 [ 0 13  0]
 [ 0  1 11]]


'''