Exercise

Set the hyperparameter grid of RF

In this exercise, you'll manually set the grid of hyperparameters that will be used to tune rf's hyperparameters and find the optimal regressor. For this purpose, you will be constructing a grid of hyperparameters and tune the number of estimators, the maximum number of features used when splitting each node and the minimum number of samples (or fraction) per leaf.

Instructions

100 XP

  • Define a grid of hyperparameters corresponding to a Python dictionary called params_rf with:

    • the key 'n_estimators' set to a list of values 100, 350, 500

    • the key 'max_features' set to a list of values 'log2', 'auto', 'sqrt'

    • the key 'min_samples_leaf' set to a list of values 2, 10, 30