cross_validate(estimator, X, y=None, *, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', return_train_score=False, return_estimator=False, error_score=nan) 通過交叉驗證評估指標,並記錄擬合/得分時間。 在用戶指南中閱讀更多信息。 參數...
也很简单,使用相同的fold,把y划分计算再平均就好了. cv=KFold(n_splits=5,shuffle=False,random_state=0)predict_y=cross_val_predict(estimator,data_x,y,cv=cv)cv=KFold(n_splits=5,shuffle=False,random_state=0)# 这是个generator,用完就没了,所以再来一遍test_index=[i[1]foriincv.split(data_x...
在训练随机森林分类器后,我尝试了两种计算 AUC 分数的方法。第一个是从 cross_validate 函数获取指标: numeric_transformer = make_pipeline( IterativeImputer(estimator=RandomForestRegressor(),random_state=0), StandardScaler() ) preprocessor = make_column_transformer( (numeric_transformer, numeric_cols) ) ...
扒了cross_validate代码的核心部分,如下: X,y,groups=indexable(X,y,groups)cv=check_cv(cv,y,classifier=is_classifier(estimator))ifcallable(scoring):scorers=scoringelifscoringisNoneorisinstance(scoring,str):scorers=check_scoring(estimator,scoring)else:scorers=_check_multimetric_scoring(estimator,scoring)#...
elif scoring is None orisinstance(scoring,str):scorers=check_scoring(estimator,scoring)else:scorers=_check_multimetric_scoring(estimator,scoring)# We clone the estimator to make sure that all the folds are # independent,and that it is pickle-able.parallel=Parallel(n_jobs=n_jobs,verbose=verbose,...
我正在使用cross_validate学习函数来适应一个RandomForest分类器.我想知道是否有一种方法可以导出合适的模型来保存它们并导入来预测新的数据。我尝试使用return_estimator=True选项 cv_results = cross_validate 浏览1提问于2019-07-05得票数 1 回答已采纳
Describe the bug When we pass a fitted estimator into cross_validate it will fit this estimator again on the given train-validation splits. However, users can pass sample_weight to the fitted estimator without being warned that it is not...
However, I wonder if it's a good pattern where we'd then need to basically patch a bunch of sklearn methods to add things to them or change their default values. To me, it would be much more natural to get the output of sklearn's cross_validate, the estimator, and the data, and...
estimator IEstimator<ITransformer> 要适应的估算器。 numberOfFolds Int32 交叉验证折叠数。 labelColumnName String 用于评估) 的标签列 (。 rowGroupColumnName String 用于对行进行分组的 groupId 列data的名称。 拆分交叉验证的数据时,此列将自动用作 SamplingKeyColumn,因为排名算法需要这样做(如果未null执行行...
sklearn.model_selection.cross_validate(estimator, X, y=None, *, groups=None, scoring=None, cv=None, n_jobs=None, verbose=0, fit_params=None, pre_dispatch='2*n_jobs', return_train_score=False, return_estimator=False, error_score=nan) ...