Evaluate ML Pipeline

Score one learner and persist the report. The pipeline, the splitter, and any non-default score are already declared. skore.evaluate is the entry point. Do not hand-roll cross_val_score, cross_validate, classification_report, or metric prints.

A SkrubLearner does not implement sklearn's fit(X, y). cross_val_score raises. Call skore.evaluate(learner, data={...}) and omit splitter=, unless translation.splitter is prefit.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Experiment markdown and # comments describe this evaluation. Questions and the close use the same data-science language: not skill ids, G-* names, or the wrapper CLI. <!-- results-embed: … --> is a site marker.