how does nested-cv compare to native nested CV in sklearn?

[ezeeetm]

First of all, thank you very much for the work on this library, its much needed and looks very well maintained.

Just wonder, what are the functional differences between using the nested-cv library, and doing native nested CV in sklearn? (like this: https://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html)

here's the interesting part of that for convenience:

# Choose cross-validation techniques for the inner and outer loops,
# independently of the dataset.
# E.g "GroupKFold", "LeaveOneOut", "LeaveOneGroupOut", etc.
inner_cv = KFold(n_splits=4, shuffle=True, random_state=i)
outer_cv = KFold(n_splits=4, shuffle=True, random_state=i)

# Non_nested parameter search and scoring
clf = GridSearchCV(estimator=svm, param_grid=p_grid, cv=inner_cv)
clf.fit(X_iris, y_iris)
non_nested_scores[i] = clf.best_score_

# Nested CV with parameter optimization
nested_score = cross_val_score(clf, X=X_iris, y=y_iris, cv=outer_cv)
nested_scores[i] = nested_score.mean()