5. Decision Trees and Random Forests

Activity 5.01: Cross-Validation Grid Search with Random Forest

Solution:

1. Create a dictionary representing the grid for the max_depth and n_estimators hyperparameters that will be searched. Include depths of 3, 6, 9, and 12, and 10, 50, 100, and 200 trees. Leave the other hyperparameters at their defaults. Create the dictionary using this code:

   rf_params = {'max_depth':[3, 6, 9, 12],
                'n_estimators':[10, 50, 100, 200]}

   Note

   There are many other possible hyperparameters to search over. In particular, the scikit-learn documentation for random forest indicates that "The main parameters to adjust when using these methods are n_estimators and max_features" and that "Empirical good default values are … max_features=sqrt(n_features) for classification tasks."

   Source: https://scikit-learn.org/stable/modules/ensemble.html...