grid_search.py

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier

def grid_search_example():
    model = GradientBoostingClassifier(random_state=0, n_estimators=50)
    
    pg = {
        'learning_rate':list(np.arange(0.1,0.4,0.1)),
        'n_estimators': list(range(50,200,50)),
        'min_samples_split': list(range(2,10,2))
    }
    
    opt = GridSearchCV(estimator=model, param_grid=pg, scoring='r2', cv=5)
    opt.fit(X_train, y_train)
    
    df = pd.DataFrame(opt.cv_results_)
    
    x = df['mean_test_score'].index
    y = df['mean_test_score']
    plot(x,y)

    print("Validation Score:", opt.best_score_)
    print("Test Score:", opt.score(X_test, y_test))