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1.1 KiB
1.1 KiB
The exercice is validated is all questions of the exercice are validated
The question 1 is validated if the code that runs the gridsearch is (the parameters may change):
parameters = {'n_estimators':[10, 50, 75],
'max_depth':[3,5,7],
'min_samples_leaf': [10,20,30]}
rf = RandomForestRegressor()
gridsearch = GridSearchCV(rf,
parameters,
cv = [(np.arange(18576), np.arange(18576,20640))],
n_jobs=-1)
gridsearch.fit(X, y)
The question 2 is validated if the function is:
def select_model_verbose(gs):
return gs.best_estimator_, gs.best_params_, gs.best_score_
In my case, the gridsearch parameters are not interesting. Even if I reduced the over-fitting of the Random Forest, the score on the test is lower than the score on the test returned by the Gradient Boosting in the previous exercise without optimal parameters search.
The question 3 is validated if the code used is:
model, best_params, best_score = select_model_verbose(gridsearch)
model.predict(new_point)