scikit-learn Cookbook , Second Edition - Second Edition

Second base model – gradient boost ensemble

4. Perform a randomized grid search on gradient-boosted trees:

from sklearn.model_selection import RandomizedSearchCV from sklearn.ensemble import GradientBoostingRegressor   param_grid = {'learning_rate': [0.1,0.05,0.03,0.01],               'loss': ['huber'],               'max_depth': [5,7,10],               'max_features': [0.4,0.6,0.8,1.0],               'min_samples_leaf': [2,3,5],               'n_estimators': [100],               'warm_start': [True], 'random_state':[7]               }  boost_gs = RandomizedSearchCV(GradientBoostingRegressor(), param_distributions = param_grid,cv=3, n_jobs=-1,n_iter=25) boost_gs.fit(X_1, y_1)

5. View the best score and parameters:

boost_gs.best_score_0.82767651150013244boost_gs.best_params_{'learning_rate': 0.1, 'loss': 'huber', 'max_depth': 10, 'max_features': ...