Differences between sklearn.model_selection.KFold and sklearn.model_selection.cross_validate with 'cv' parameter?
I believe that KFold will simply carve your training data into 10 splits.
cross_validate, however, will also carve the data into 10 splits (with the cv=10 parameter) but it will also actually perform the cross-validation. In other words, it will run your model 10x and you will be able to report on the performance of your model, which KFold does not do.
In other words, KFold is 1 small step in cross_validation.
Can I use cross_validate in sklearn with cv=10 to instead of using Kfold with n_splits=10? Does they work as same?