本文整理汇总了Python中sklearn.model_selection.GridSearchCV.predict_log_proba方法的典型用法代码示例。如果您正苦于以下问题：Python GridSearchCV.predict_log_proba方法的具体用法？Python GridSearchCV.predict_log_proba怎么用？Python GridSearchCV.predict_log_proba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.model_selection.GridSearchCV的用法示例。
在下文中一共展示了GridSearchCV.predict_log_proba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_stochastic_gradient_loss_param
# 需要导入模块: from sklearn.model_selection import GridSearchCV [as 别名]
# 或者: from sklearn.model_selection.GridSearchCV import predict_log_proba [as 别名]
def test_stochastic_gradient_loss_param():
# Make sure the predict_proba works when loss is specified
# as one of the parameters in the param_grid.
param_grid = {
'loss': ['log'],
}
X = np.arange(24).reshape(6, -1)
y = [0, 0, 0, 1, 1, 1]
clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
param_grid=param_grid)
# When the estimator is not fitted, `predict_proba` is not available as the
# loss is 'hinge'.
assert_false(hasattr(clf, "predict_proba"))
clf.fit(X, y)
clf.predict_proba(X)
clf.predict_log_proba(X)
# Make sure `predict_proba` is not available when setting loss=['hinge']
# in param_grid
param_grid = {
'loss': ['hinge'],
}
clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
param_grid=param_grid)
assert_false(hasattr(clf, "predict_proba"))
clf.fit(X, y)
assert_false(hasattr(clf, "predict_proba"))