交叉验证和模型选择

Question

我正在使用skilearn进行SVM培训.我正在使用交叉验证来评估估算器并避免过度拟合模型.

我将数据分成两部分.训练数据和测试数据.这是代码:

import numpy as np
from sklearn import cross_validation
from sklearn import datasets
from sklearn import svm
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
    iris.data, iris.target, test_size=0.4, random_state=0
)
clf = svm.SVC(kernel='linear', C=1)
scores = cross_validation.cross_val_score(clf, X_train, y_train, cv=5)
print scores

# Now I need to evaluate the estimator *clf* on X_test.
clf.score(X_test,y_test)
# here,  I get an error say that the model is not fitted using fit(), but normally,
# in cross_val_score function the model is fitted? What is the problem?

Answer 1

cross_val_score基本上是sklearn 交叉验证迭代器的便利包装器.您给它一个分类器和您的整个(训练+验证)数据集,它通过将您的数据分成随机训练/验证集,拟合训练集,并在验证集上计算得分,自动执行一轮或多轮交叉验证.有关示例和更多说明,请参阅此处的文档.

clf.score(X_test, y_test)引发异常的原因是因为在估计器cross_val_score的副本而不是原始副本上执行拟合(请参阅此处clone(estimator)的源代码中的使用).因此,在函数调用之外保持不变,因此在调用时未正确初始化.clfclf.fit