gbrt gradientboostingclassifier(random_state= max_depth= gbrt fit(x_trainy_trainprint("accuracy on training set{ }format(gbrt score(x_trainy_train))print("accuracy on test set{ }format(gbrt score(x_testy_test))out[ ]accuracy on training set accuracy on test set in[ ]gbrt gradientboostingclassifier(random_state= learning_rate= gbrt fit(x_trainy_trainprint("accuracy on training set{ }format(gbrt score(x_trainy_train))print("accuracy on test set{ }format(gbrt score(x_testy_test))out[ ]accuracy on training set accuracy on test set both methods of decreasing the model complexity reduced the training set accuracyas expected in this caselowering the maximum depth of the trees provided significant improvement of the modelwhile lowering the learning rate only increased the generalization performance slightly as for the other decision tree-based modelswe can again visualize the feature importances to get more insight into our model (figure - as we used treesit is impractical to inspect them alleven if they are all of depth in[ ]gbrt gradientboostingclassifier(random_state= max_depth= gbrt fit(x_trainy_trainplot_feature_importances_cancer(gbrt supervised learning