python 交叉验证后获取模型_python – predict_proba用于交叉验证的模型

我想用Logistic回归模型预测交叉验证的概率.我知道您可以获得交叉验证分数,但是可以从predict_proba而不是分数返回值吗？

# 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)

解决方法:

现在,这是作为scikit-learn版本0.18的一部分实现的.您可以将’method’字符串参数传递给cross_val_predict方法.文档是here.

例：

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

另请注意,这是新sklearn.model_selection包的一部分,因此您需要进行以下导入：

from sklearn.model_selection import cross_val_predict

标签：cross-validation,logistic-regression,python,scikit-learn

来源： https://codeday.me/bug/20190928/1828505.html