feat: stacking ensembling of the top-k AutoML candidates#14
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Modeling-depth batch 2: AutoML(ensemble=True) stacks the top-k leaderboard candidates into one model via a cross-fit meta-learner (EnsemblePort + StackingEnsemble: sklearn StackingClassifier/Regressor). Off by default (classical-first unchanged); composes with calibrate; DI-resolvable via from_context. Refit path refactored to a _build_winner helper. TDD on real breast_cancer data; docs note in automl.md.
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Modeling-depth batch 2 (TDD, real data).
AutoML(ensemble=True)stacks the top-k leaderboard candidates into one stronger model via a cross-fit meta-learner — the standard last-mile AutoML lift.EnsemblePort+StackingEnsemble(sklearnStackingClassifier/StackingRegressor, logistic/ridge meta-learner over the top-ensemble_sizetrainers)._build_winnerhelper (single-best or stack), then the existing calibration step applies — soensembleandcalibratecompose.from_context.Tests (real
breast_cancer, no fakes)ensemble=True→ winner is aStackingClassifiernamedstacking_ensemble, stacks ≥2 members, competitive holdout ROC-AUC;ensemble=False→ single model (unchanged).Local gates green: ruff/format/pyright(0)/9 tests (ensemble+automl+calibration) + strict docs.