Publish MargaDrishti models, cards and reports

README.md

@@ -21,7 +21,8 @@ Live dashboard: **[adarshcod30/margadrishti](https://huggingface.co/spaces/adars
 ## Read this before using the numbers
 
 **These models rank predicted _enforcement demand_, not where illegal parking
-occurs.** `corr(log patrol_hours, log total_captures) = 0.967` — about 94% of the
+occurs.** `corr(log patrol_hours, log total_captures) = 0.96` across all 2,534
+res-9 cells — about 93% of the
 variance in per-cell violation counts is explained by how much patrolling
 happened there. The project's own
 [enforcement-bias audit](https://github.com/adarshcod30/MargaDrishti/blob/main/reports/fairness_audit.md)
@@ -65,11 +66,11 @@ accuracy.
 
 | Target | Best family | Metric | vs strongest baseline |
 |---|---|---|---|
-| A1 · parking intensity | xgboost | 0.5014 Poisson deviance | **+42.9%** |
-| A2 · hotspot | catboost | 0.1438 PR-AUC | ~49× base rate (0.29%) |
+| A1 · parking intensity | xgboost | 0.4999 Poisson deviance | **+43.0%** |
+| A2 · hotspot | random forest | 0.1365 PR-AUC | 46.9× base rate (0.291%) |
 | A3 · capture rejection | xgboost | 0.3088 PR-AUC | *not answerable* |
 | B1 · clearance duration | xgboost | 0.4990 ± 0.128 | +27.7% |
-| B2 · road closure | catboost | 0.3615 ± 0.071 PR-AUC | ~4× (8.3% prevalence) |
+| B2 · road closure | catboost | 0.3615 ± 0.071 PR-AUC | 4.1× (8.7% prevalence) |
 | B4 · cause from text | char n-gram + logreg | 0.478 macro-F1 | 10.8× |
 
 B4's notable result is equity rather than accuracy: **English 0.483 vs Kannada