use 100 instead of 800 n_estimators; looks the same but faster; ESLII uses 800

Author: pprett

examples/ensemble/plot_partial_dependence.py

@@ -67,7 +67,7 @@
 
 print('_' * 80)
 print("Training GBRT...")
-clf = GradientBoostingRegressor(n_estimators=800, max_depth=4,
+clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,
                                 learning_rate=0.1, loss='huber',
                                 random_state=1)
 clf.fit(X_train, y_train)