spb/metrika Public
Stata-class statistics, GPU-accelerated by Apple Silicon. Native Swift — no Electron, no Python runtime, no compromises.
Swift 92.4%
HTML 3.3%
R 3%
Shell 1.3%
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feat(stats): gradient-boosted regression trees (boost command)
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- ZQGradientBoosting: exact-greedy trees cloning xgboost's algorithm — gain 1/2[GL2/(HL+l) + GR2/(HR+l) - G2/(H+l)] - gamma, leaf -G/(H+l), midpoint splits between consecutive distinct values, missing rows default left, pre-sorted feature indices; squared loss, deterministic (no subsampling) - engine: 'boost y x…, rounds(#) [eta() maxdepth() lambda()]' reporting training R2/RMSE with an in-sample caveat; model stored in the estimation state (Kind.boost) so predict routes through the trees (missing features follow the default direction); margins and GLM statistics refused after boost - validation: per-observation prediction parity with R xgboost 3.2 (exact method, base_score = mean) at 1e-4 (xgboost is float32 internally); a stump finds the exact midpoint split with lambda 0; training loss decreases monotonically in rounds - manual entry + coverage test - 115 tests green (swift test and xcodebuild with GPU suites) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>