python - predict_proba 用于交叉验证模型

Question

我想通过交叉验证从逻辑回归模型预测概率。我知道您可以获得交叉验证分数，但是否可以从 predict_proba 返回值而不是分数？

# imports
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import (StratifiedKFold, cross_val_score,
                                      train_test_split)
from sklearn import datasets

# setup data
iris = datasets.load_iris()
X = iris.data
y = iris.target

# setup model
cv = StratifiedKFold(y, 10)
logreg = LogisticRegression()

# cross-validation scores
scores = cross_val_score(logreg, X, y, cv=cv)

# predict probabilities
Xtrain, Xtest, ytrain, ytest = train_test_split(X, y)
logreg.fit(Xtrain, ytrain)
proba = logreg.predict_proba(Xtest)

Answer 1

这现在作为 scikit-learn 0.18 版的一部分实现。您可以将“方法”字符串参数传递给 cross_val_predict 方法。文档是 here .

例子:

proba = cross_val_predict(logreg, X, y, cv=cv, method='predict_proba')

另请注意，这是新的 sklearn.model_selection 包的一部分，因此您将需要此导入:

from sklearn.model_selection import cross_val_predict