Applied Deep Learning with Python

Training a Random Forest

Observe how similar it is to train and make predictions on each model, despite them each being so different internally.

1. Train a Random Forest classification model composed of 50 decision trees, each with a max depth of 5. Run the cell containing the following code:

       from sklearn.ensemble import RandomForestClassifier       forest = RandomForestClassifier(n_estimators=50,       max_depth=5,       random_state=1)       forest.fit(X_train, y_train)       check_model_fit(forest, X_test, y_test) 

Note the distinctive axes-parallel decision boundaries ...