本文整理汇总了Python中sklearn.linear_model.logistic.LogisticRegression._predict_proba_lr方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression._predict_proba_lr方法的具体用法？Python LogisticRegression._predict_proba_lr怎么用？Python LogisticRegression._predict_proba_lr使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.linear_model.logistic.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression._predict_proba_lr方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_logreg_predict_proba_multinomial
# 需要导入模块: from sklearn.linear_model.logistic import LogisticRegression [as 别名]
# 或者: from sklearn.linear_model.logistic.LogisticRegression import _predict_proba_lr [as 别名]
def test_logreg_predict_proba_multinomial():
X, y = make_classification(n_samples=10, n_features=20, random_state=0, n_classes=3, n_informative=10)
# Predicted probabilites using the true-entropy loss should give a
# smaller loss than those using the ovr method.
clf_multi = LogisticRegression(multi_class="multinomial", solver="lbfgs")
clf_multi.fit(X, y)
clf_multi_loss = log_loss(y, clf_multi.predict_proba(X))
clf_ovr = LogisticRegression(multi_class="ovr", solver="lbfgs")
clf_ovr.fit(X, y)
clf_ovr_loss = log_loss(y, clf_ovr.predict_proba(X))
assert_greater(clf_ovr_loss, clf_multi_loss)
# Predicted probabilites using the soft-max function should give a
# smaller loss than those using the logistic function.
clf_multi_loss = log_loss(y, clf_multi.predict_proba(X))
clf_wrong_loss = log_loss(y, clf_multi._predict_proba_lr(X))
assert_greater(clf_wrong_loss, clf_multi_loss)