本文整理汇总了Python中sklearn.pipeline.Pipeline.score_方法的典型用法代码示例。如果您正苦于以下问题：Python Pipeline.score_方法的具体用法？Python Pipeline.score_怎么用？Python Pipeline.score_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.pipeline.Pipeline的用法示例。
在下文中一共展示了Pipeline.score_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: fit
# 需要导入模块: from sklearn.pipeline import Pipeline [as 别名]
# 或者: from sklearn.pipeline.Pipeline import score_ [as 别名]
def fit(X, Y):
models = {
'LogisticRegression': LogisticRegression(),
'GradientBoostingClassifier': GradientBoostingClassifier(n_estimators=150),
'RandomForestClassifier': RandomForestClassifier(n_estimators=150)
}
best_score = 0
best_model = ''
for model in models:
vec = DictVectorizer(sparse=False)
clf = models[model]
pl = Pipeline([('vec', vec), ('clf', clf)])
#TODO: grid search for model params
scores = cross_val_score(pl, X, Y, n_jobs=3)
if scores.mean() > best_score:
best_score = scores.mean()
best_model = model
# retrain best model with all data
vec = DictVectorizer(sparse=False)
clf = models[best_model]
pl = Pipeline([('vec', vec), ('clf', clf)])
pl.fit(X, Y)
pl.score_ = best_score # report cv score
return pl