sklearn中cross_val_score、cross_val_predict的用法比较

交叉验证的概念，直接粘贴scikit-learn官网的定义：

scikit-learn中计算交叉验证的函数：

cross_val_score：得到K折验证中每一折的得分，K个得分取平均值就是模型的平均性能

cross_val_predict：得到经过K折交叉验证计算得到的每个训练验证的输出预测

方法：

cross_val_score：分别在K-1折上训练模型，在余下的1折上验证模型，并保存余下1折中的预测得分

cross_val_predict：分别在K-1上训练模型，在余下的1折上验证模型，并将余下1折中样本的预测输出作为最终输出结果的一部分

结论：

cross_val_score计算得到的平均性能可以作为模型的泛化性能参考

cross_val_predict计算得到的样本预测输出不能作为模型的泛化性能参考

代码样例：

from sklearn import datasets
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn import datasets
import numpy as np
from sklearn.tree import DecisionTreeClassifier

# 加载鸢尾花数据集
iris = datasets.load_iris()
iris_train = iris.data
iris_target = iris.target
print(iris_train.shape)
print(iris_target.shape)
(150, 4)
(150,)

# 构建决策树分类模型
tree_clf = DecisionTreeClassifier()
tree_clf.fit(iris_train, iris_target)
tree_predict = tree_clf.predict(iris_train)
​
# 计算决策树分类模型的准确率
from sklearn.metrics import accuracy_score
print("Accuracy:", accuracy_score(iris_target, tree_predict))
Accuracy: 1.0

# 交叉验证cross_val_score输出每一折上的准确率
from sklearn.model_selection import cross_val_predict, cross_val_score, cross_validate
tree_scores = cross_val_score(tree_clf, iris_train, iris_target, cv=3)
print(tree_scores)
[0.98039216 0.92156863 1.        ]

# 交叉验证cross_val_predict输出每个样本的预测结果
tree_predict = cross_val_predict(tree_clf, iris_train, iris_target, cv=3)
print(tree_predict)
print(len(tree_predict))
print(accuracy_score(iris_target, tree_predict))
[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 2 1 1 1
 1 1 1 2 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 1 2 2 2 2
 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2
 2 2]
150
0.96

print(tree_clf.predict(iris_train))
[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]

# 交叉验证cross_validate对cross_val_score结果进行包装，并包含fit的时间等信息
tree_val = cross_validate(tree_clf, iris_train, iris_target, cv=3)
print(tree_val)
{'fit_time': array([0., 0., 0.]), 'score_time': array([0., 0., 0.]), 'test_score': array([0.98039216, 0.92156863, 0.97916667])}

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交叉验证评价方式scoring的参数链接：3.3. Metrics and scoring: quantifying the quality of predictions — scikit-learn 1.0.2 documentation