v1.1.0 — crown-week release

The crown-week release: measured parity-or-better against xgboost, lightgbm, and catboost, plus a scikit-learn-shaped Python surface.

Added

• BonsaiClassifier — sklearn-style classifier over the engine's logloss (binary) and softmax (multiclass) objectives. Binary predict_proba; multiclass predict returns labels (multiclass predict_proba is a tracked follow-up). Arbitrary label types are encoded/decoded via classes_.
• scikit-learn estimator compatibility for BonsaiRegressor/BonsaiClassifier — get_params/set_params/score, and drop-in use in clone, Pipeline, GridSearchCV, cross_val_score, and pickle — implemented without a scikit-learn runtime dependency (import bonsai never imports sklearn).
• sample_weight on BonsaiRegressor.fit / bonsai.train (sklearn convention) — per-row weighting of gradients and hessians.
• OrderedTargetEncoder — leak-free ordered target statistics for categorical features, including cross=2 pair encodings (decision 58; guide chapter 13).
• Poisson regression objective (closes #44).

Changed / Performance

• Binning: one shared row sample for the whole matrix instead of a per-feature reservoir pass — 24× faster mapper-fit at 16M, quality-neutral (decision 64).
• CPU fill loop software prefetch — the 16M-row fit now ties xgboost-hist (decision 61).
• Fresh same-pod re-baseline (decision 62–64): bonsai's GPU oblivious grower now edges catboost and beats xgboost-GPU at 16M at matched accuracy and ~3× less host memory, and holds the fastest slot at every row scale.

Fixed

• GPU oblivious grower carried a split-selection defect (a missing port of the issue-#60 fix) that silently cost ~0.011 test r² at depth ≥ 5; now matches its CPU twin exactly (decision 63).
• Cross-architecture bit determinism: models are byte-identical across arm64/x86-64 at a fixed thread count — no floating-point contraction on the host plane (decisions 59–60), enforced by a cross-arch CI gate.
• OpenMP build variance can no longer be silent — a missing OpenMP is now a hard configure error, not a quiet serial fallback that changed model bits (decision 60).
• Quality-campaign correctness fixes: count-weighted cuts for heavy-value columns (#63/decision 57), one cut per distinct value on duplicate-heavy columns (#61), infeasible frontier nodes contribute zero gain rather than vetoing the split (#60), and the true diagonal softmax hessian p(1−p) (#62).

Docs

• New guide material (categoricals, structure-vs-scheme) and a claims-and-proofs table in the README linking every performance/quality claim to a reproducible run and the decision that records it.
• Note: pyproject.toml had drifted to 0.6.0 after the v1.0.0 tag; corrected to 1.1.0 here.