GBDT_solver.py 文件源码

def test_model_select_by_param():
    iris = load_iris()    
    gbdt = GradientBoostingRegressor() 
    parameters = {'n_estimators': [1000, 5000], 'max_depth':[3,4]}
    grid_search = GridSearchCV(estimator=gbdt, param_grid=parameters, cv=10, n_jobs=-1)
    print("parameters:")
    pprint.pprint(parameters)
    grid_search.fit(iris.data[:150],iris.target[:150])
    print("Best score: %0.3f" % grid_search.best_score_)
    print("Best parameters set:")
    best_parameters=grid_search.best_estimator_.get_params()
    for param_name in sorted(parameters.keys()):
        print("\t%s: %r" % (param_name, best_parameters[param_name]))