[英]Using cross-validation to determine weights of machine learning algorithms (GridSearchCv,RidgeCV,StackingClassifier)
My question has to do with GridSearchCV, RidgeCV, and StackingClassifier/Regressor.我的问题与 GridSearchCV、RidgeCV 和 StackingClassifier/Regressor 有关。
My question is, what exactly does this mean?我的问题是,这到底是什么意思? Does it break the train data into k folds, and then for each fold, train the final estimator on the training section of the fold, test it on the testing section of the fold, and then take the final estimator weights from the fold with the best score?
它是否将训练数据分成 k 折,然后对于每折,在折的训练部分训练最终估计器,在折的测试部分对其进行测试,然后从折中获取最终估计器的权重最佳得分? or what?
或者是什么?
-To find the best hyperparameters, do they do a CV on all the folds, for each hyperparameter, find the hyperparameters that had the best average score AND THEN AFTER finding the best hyperparameters, train the model with the best hyperparameters, using the WHOLE training set? - 要找到最佳超参数,他们是否对所有折叠进行 CV,对于每个超参数,找到具有最佳平均得分的超参数,然后在找到最佳超参数后,使用最佳超参数训练 model,使用整个训练放? Or am I looking at it wrong?
还是我看错了?
If anyone could shed some light on this, that would be great.如果有人能对此有所了解,那就太好了。 Thanks!
谢谢!
You're exactly right.你完全正确。 The process looks like this:
该过程如下所示:
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