Branch-AI/one_exercise_per_file/week02/day05/ex03/audit/readme.md

The exercice is validated is all questions of the exercice are validated

The question 1 is validated if the code that runs the grid search is similar to:

parameters = {'n_estimators':[10, 50, 75],
            'max_depth':[4, 7, 10]}

rf = RandomForestRegressor()
gridsearch = GridSearchCV(rf,
                        parameters,
                        cv = 5,
                        n_jobs=-1,
                        scoring='neg_mean_squared_error')

gridsearch.fit(X_train, y_train)

The answers that uses another list of parameters are accepted too !

The question 2 is validated if you called this attributes:

print(gridsearch.best_score_)
print(gridsearch.best_params_)
print(gridsearch.cv_results_)

The best score is -0.29028202683007526, that means that the MSE is ~0.29, it doesn't give any information since this metric is arbitrary. This score is the average of neg_mean_squared_error on all the validation sets.

The best models params are {'max_depth': 10, 'n_estimators': 75}.

As you may must have a different parameters list than this one, you should have different results.

The question 3 is validated if you used the fitted estimator to compute the score on the test set: gridsearch.score(X_test, y_test). The MSE score is ~0.27. The score I got on the test set is close to the score I got on the validation sets. It means the models is not over fitted.