0.19.0

Added

• With verbose=True, fit prints a notice when early_stopping=True silently holds out validation_fraction of the training rows as the validation set, so the effective training size is visible. Default verbose=False unchanged.

Changed

• Classifier leaf_estimation_iterations default is now None (auto) instead of a concrete 3; it resolves to 3 and fits bit-identically. An A/B across the decision suites confirmed 3 is the right effective value (helps small/categorical binary, inert where linear leaves take over or for multiclass) — None just stops the API from advertising a refinement count that is dead in those regimes.

Fixed

• sample_weight is now honored everywhere, not just in the gradient step: the ordered-target categorical encoder, the quantile bin borders, and the early-stopping validation metric on the auto-split (or bagged out-of-bag) holdout. Previously a weight-0 row still shaped encodings and bin edges and scored the early-stopping metric at full weight — up to an order-of-magnitude model degradation in an adversarial repro. sample_weight=None and uniform weights stay bitwise-identical to before. eval_set now accepts an optional third (X, y, sample_weight) element.
• The inert-knob warnings for leaf_estimation_iterations now fire for any explicitly-set value > 1 that will be ignored on the path about to run; the auto default stays quiet.