Add Information Value (IV) / Weight-of-Evidence binning as metric and feature-selection

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Describe the workflow you want to enable

Rank a feature's predictive strength against a binary target by binning it (equal-frequency or optimized) and computing Information Value: sum((pct_event - pct_nonevent) * log(pct_event / pct_nonevent)) over bins.

This is a standard univariate feature-screening step in binary classification, conceptually adjacent to things sklearn already ships in sklearn.feature_selection (mutual_info_classif, f_classif, chi2) as a way to rank/filter features before modeling. Notably this metric does not depends on imbalance.

Describe your proposed solution

'sklearn.metric.IV(X,y, bins=10) would be useful for establishing Information value. Then 'sklearn.feature_selection.information_value(X, y, bins=10)' returning per-feature IV, using equal-frequency quantile binning by default (fast, matches the common textbook definition).

Describe alternatives you've considered, if relevant

Checked scikit-learn's issue tracker for "Information Value" and "Weight of Evidence" - found nothing (no open request, no closed-as-declined). The realistic existing option is optbinning, a purpose-built, actively maintained package that solves actual bin optimization (via a CP/MIP solver) rather than plain quantile cuts.

IV/WOE binning is old and widely used, but specifically within credit-scoring-style binary classification rather than general ML - similar concern to the adjacent PSI request I'm also filing. Raising it mainly to get a maintainer judgment on record, since I couldn't find this discussed anywhere in scikit-learn's history.

Additional context

mutual_info_classif already covers "how predictive is this feature" in a distribution-agnostic way; IV specifically gives interpretable, additive-across-bins output that credit-risk practitioners rely on for regulatory model documentation, which is the main reason it persists as a distinct metric rather than being subsumed by mutual information in that field.

No source file or test is named. Start by reading the contributing guidelines and the existing sklearn.feature_selection entries for mutual_info_classif, f_classif, and chi2; the issue needs maintainer agreement on scope and API before implementation can be considered done.