grid search experiment.py

model = RandomForestRegressor(n_jobs=-1, random_state=42, verbose=2)
 
grid = {'n_estimators': [10, 13, 18, 25, 33, 45, 60, 81, 110, 148, 200],
        'max_features': [0.05, 0.07, 0.09, 0.11, 0.13, 0.15, 0.17, 0.19, 0.21, 0.23, 0.25],
        'min_samples_split': [2, 3, 5, 8, 13, 20, 32, 50, 80, 126, 200]}
 
rf_gridsearch = GridSearchCV(estimator=model, param_grid=grid, n_jobs=4, 
        cv=cv, verbose=2, return_train_score=True)
 
rf_gridsearch.fit(X1, y1)
 
# after several hours
df_gridsearch = pd.DataFrame(rf_gridsearch.cv_results_)