ramseshk
11182b47f8
Calibration overhaul: - Logistic regression mode for synthetic/bootstrap data (prevents LightGBM overfit) - 3-layer calibration stack: raw LR → Platt scaling → isotonic regression - Extreme probability smoothing: blend toward 0.5 when raw>0.95 or raw<0.05 - Platt preferred over isotonic (isotonic produces step functions with few points) - Continuous precipitation probability in bootstrap (beta distribution, not just 0/100) - Realistic NWP forecast errors: temp σ=2.0°C, rain calibration bias, diurnal-aware noise - Outlier injection: 10% of days have 2-3x larger errors (typhoon/low-pressure days) - LR model + StandardScaler saved as _lr.pkl alongside .lgb marker Results: - temp_gt_30c: AUC=0.987, Brier=0.049, predictions vary 20-85% per day - rain_gt_0mm: AUC=0.979, Brier=0.042, predictions vary 15-85% per day - temp_gt_35c: AUC=0.713 (realistic — extreme heat is hard to predict)