v3.0.2

Added

• Glob column drops: columns_to_drop accepts patterns alongside exact names — "target_*" drops every matching column. A pattern never drops the target itself, and a pattern matching nothing raises to catch typos. Works in get_data and get_hf_data.
• Probe metrics: feature_ranking(probe=True) (the default) evaluates every method's ranking with a shared cross-validated linear probe (standardized logistic regression or ridge, 3-fold, top-k cuts 1-64, at most 10,000 seeded rows). Per-method reports land in diagnostics[method]["probe"] and result.probe_table().
• Auto-tuned vote weights: voting(result, weights="auto") weights each method by its probe skill, so more predictive methods vote harder; all-at-chance falls back to equal weights.
• Per-method convex metrics: RankingResult.fit_convex fills ConvexFit.method_metrics with the fit metric recomputed on each ranking method's own top_n selection beside the returned "ensemble" fit.

Changed

• fit_convex clamps a top_n above the feature count to all features instead of raising, so a fixed NUM_KEEP works across datasets of any width.