How to get best_estimator parameters from GridSearch using cross_val_score?

Question

I want to know the result of the GridSearch when I'm using nested cross validation with cross_val_score for convenience.

When using cross_val_score, you get an array of scores. It would be useful to receive the fitted estimator back or a summary of the chosen parameters for that estimator.

I know you can do this yourself but just implementing cross-validation manually but it is much more convenient if it can be done in conjunction with cross_val_score.

Any way to do it or is this a feature to suggest?

Answer 1

The GridSearchCV class in scikit-learn already does cross validation internally. You can pass any CV iterator as the cv argument of the constructor of GridSearchCV.