param_grid_rfr={'n_estimators':[130, 100, 70, 50],
            'max_depth':[1, 3, 5, 7],
            'criterion': ['mse', 'mae'],
            'min_samples_split':[2, 5, 7],
            'min_samples_leaf':[3, 5, 1, 7, 2],
            'max_features':[1.0 ,0.3, 0.2, 0.4],
            'bootstrap': [True]
            }
rfr = RandomForestRegressor()
grid_rfr = GridSearchCV(estimator=rfr, param_grid=param_grid_rfr, scoring='neg_mean_absolute_error', cv=10, verbose=10)
grid_rfr.fit(X_filtered, Y_scaled)
print(grid_rfr.best_score_)
print(grid_rfr.best_estimator_)