So your score for the grid search is going to be worse than your baseline.

Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of X_val per fold).

It's working with less data since you have split the X_train sample.

The grid searched model is at a disadvantage because: This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen. Then you're using the fitted model to score the X_train sample. Your baseline model used X_train to fit the model.

Set board from previous board to next player.

Find the first missing non - negative number of elements in the input array.

Create a unique filename from a directory.

Find all the functions that are reachable.

This is intended to give you an instant insight into BetaGo implemented functionality, and help decide if they suit your requirements.

Kandi has reviewed BetaGo and discovered the below as its top functions.

Betago python