Model/services/ml_training_data
Khalim Conn-Kowlessar 700ff4640c slice 16g: LightGBM objective=mape for sap_score + peui_ucl
Per ADR-0008: the v15 baseline reports MAPE but optimises MSE, which
under-weights tail rows. Switching to objective='mape' applies gradient
proportional to 1/|y| and lets the model focus where MAPE penalises.

Targets co2_emissions, space_heating_kwh, hot_water_kwh, and peui_raw
retain the default 'regression' objective (some rows have ~zero CO2 from
heavy PV; MAPE objective destabilises near zero).

Sample weights deferred to slice 16i if slice 16h's per-decile residuals
still show tail bias after the objective switch.
2026-05-17 12:06:13 +00:00
..
src/ml_training_data slice 16g: LightGBM objective=mape for sap_score + peui_ucl 2026-05-17 12:06:13 +00:00
tests slice 16g: LightGBM objective=mape for sap_score + peui_ucl 2026-05-17 12:06:13 +00:00
pyproject.toml slice 14g: remote_bulk_fetcher extracts ZIP entries via HTTP Range (no full download) 2026-05-16 19:16:52 +00:00