Exercise

Grid search

Recall that we've created a baseline Gradient Boosting model in the previous lesson. Your goal now is to find the best max_depth hyperparameter value for this Gradient Boosting model. This hyperparameter limits the number of nodes in each individual tree. You will be using K-fold cross-validation to measure the local performance of the model for each hyperparameter value.

You're given a function get_cv_score(), which takes the train dataset and dictionary of the model parameters as arguments and returns the overall validation RMSE score over 3-fold cross-validation.

Instructions

100 XP

Specify the grid for possible max_depth values with 3, 6, 9, 12 and 15.
Pass each hyperparameter candidate in the grid to the model params dictionary.