How does training of sklearn Stacking metaclassifier work?

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In the docs it is said that metaclassifier is trained through cross_val_predict. From my perspective it means that data is splitten by folds, and all base estimators predict values on one fold, trained on all other folds. And that procedure goes for every fold. Then metaclassifier is trained on predictions of base estimators on these folds. Is it correct? If so, doesn't it contradict to

Note that estimators_ are fitted on the full X

in the way that base estimators are trained on several folds, not full X?

scikit-learn machine-learning python
1 Answers

There is no contradiction, because estimators_ is not used when training the metaclassifier. After the cross-val-predictions are made, you don't actually have fitted base estimators (or rather, you have multiple copies of each, depending on your cv parameter). For predicting on new data, you need a single fitted copy of each base estimator; those are obtained by fitting on the full X, and are stored in the attribute estimators_.

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