How to access Scikit Learn nested cross-validation scores

Question

I\'m using python and I would like to use nested cross-validation with scikit learn. I have found a very good example:

NUM_TRIALS = 30
non_nested_scores = np         


        

Answer 1

You cannot access individual params and best params from cross_val_score. What cross_val_score does internally is clone the supplied estimator and then call fit and score methods on it with given X, y on individual estimators.

If you want to access the params at each split you can use:

#put below code inside your NUM_TRIALS for loop
cv_iter = 0
temp_nested_scores_train = np.zeros(4)
temp_nested_scores_test = np.zeros(4)
for train, test in outer_cv.split(X_iris):
    clf.fit(X_iris[train], y_iris[train])
    temp_nested_scores_train[cv_iter] = clf.best_score_
    temp_nested_scores_test[cv_iter] = clf.score(X_iris[test], y_iris[test])
    #You can access grid search's params here
nested_scores_train[i] = temp_nested_scores_train.mean()
nested_scores_test[i] = temp_nested_scores_test.mean()