Using the best predictor from Randomized search for test data

Question

I used the 'RandomizedSearchCV' function to estimate my optimal parameters for a random forest model. Can I use the 'best_estimator_' attribute to predict on my test data?

The question I have is, while doing the randomized search, part of the data would have been used for validation. So the best estimate RF model wouldn't have been trained on the entire data set rt? Or is it all taken care of under the hood?

Answer 1

As written in the documentation cv.best_estimator_ returns the estimator that was chosen by the search, i.e. estimator which gave highest score.

If the parameter refit is set to True (default value), the model will be refit the model using the best parameter on the whole dataset including validation. Therefore you can simply use the cv.best_estimator_ to predict on your test data.