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1.4 KiB
1.4 KiB
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 these 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.