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Filtering warnings should be the last of the options. In this case I was thinking more of modifying the plot_cv_indices function to something like this:

    use_groups = 'Group' in type(cv).__name__
    groups = group if use_groups else None

    # Generate the training/testing visualizations for each CV split
    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=groups)):
    ...

Filtering warnings should be the last of the options. In this case I was thinking more of modifying the plot_cv_indices function to something like this:

    use_groups = 'Group' in type(cv).__name__
    groups = group if use_groups else None

    # Generate the training/testing visualizations for each CV split
    for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=groups)):
    ...

Thanks for the feedback, @ArturoAmorQ. If I may ask, why is removing all the warnings from this doc page not a good idea?

If I may ask, why is removing all the warnings from this doc page not a good idea?

Because of several possibilities:

• in the future we may have new warnings coming from other lines of code that we don't want to overlook, for instance, coming from deprecations in other libraries;
• if we silence them in the whole example, another contributor may change code that would otherwise rise a warning for a good reason;
• simply because warnings are sometimes introduced for enforcing good practices, by solving them, we adopt such good practices.