v2: updated model card

README.md

@@ -1,20 +1,18 @@
 ---
 library_name: sklearn
 tags:
+  - fairrelay
   - logistics
   - xgboost
-  - route-optimization
   - sklearn
   - tabular-regression
   - workload
-  - fairrelay
 datasets:
   - Cainiao-AI/LaDe-D
-  - electricsheepafrica/africa-synth-retail-and-ecommerce-last-mile-delivery-data-nigeria
 license: mit
 ---
 
-# FairRelay — Workload Scoring Model
+# FairRelay — Workload Scoring Model (v2)
 
 Part of the **[FairRelay](https://github.com/MUTHUKUMARAN-K-1/FairRelay)** AI logistics platform.
 
@@ -22,29 +20,43 @@ Part of the **[FairRelay](https://github.com/MUTHUKUMARAN-K-1/FairRelay)** AI lo
 
 Workload Scoring Model
 
-**Type**: XGBRegressor Pipeline  
-**Framework**: scikit-learn Pipeline + XGBoost  
+**Version**: v2 — Retrained with realistic, harder data to prevent overfitting and improve real-world robustness.
+
+**Type**: XGBoost Pipeline (StandardScaler + XGBoost)
 **Task**: Regression
 
+### v2 Improvements Over v1
+
+- **Hidden confounders**: Weather, traffic, building access affect ground truth but aren't in features
+- **Heteroscedastic noise**: Harder cases have more unpredictable outcomes
+- **Non-linear interactions**: Weight × stairs, packages × rain compound effects
+- **Measurement error**: Features have ±5-15% sensor/estimation noise
+- **Boundary ambiguity**: Near-threshold cases have noisy labels (simulating dispatcher disagreement)
+- **Diverse distributions**: Normal, skewed, bimodal, heavy-tail effort patterns
+
 ## Performance
 
-- **R²**: 0.9969
-- **MAE**: 4.2338
-- **RMSE**: 5.3418
-- **CV R² (5-fold)**: 0.9969 ± 0.0001
+- **R²**: 0.7536
+- **MAE**: 66.52
+- **RMSE**: 87.59
+- **Train-Test Gap**: 0.0746
+- **CV R² (5-fold)**: 0.7582 ± 0.0031
 
 
 ## Input Features
 
 | Feature | Importance |
 |---------|-----------|
-| `num_packages` | 0.0205 |
-| `total_weight_kg` | 0.0118 |
-| `num_stops` | 0.3539 |
-| `avg_fragility` | 0.0035 |
-| `total_distance_km` | 0.0008 |
-| `route_difficulty_score` | 0.5454 |
-| `estimated_time_minutes` | 0.0640 |
+| `num_packages` | 0.0373 |
+| `total_weight_kg` | 0.0161 |
+| `num_stops` | 0.7064 |
+| `avg_fragility` | 0.0145 |
+| `total_distance_km` | 0.0103 |
+| `route_difficulty_score` | 0.1742 |
+| `estimated_time_minutes` | 0.0101 |
+| `packages_per_stop` | 0.0101 |
+| `weight_per_package` | 0.0116 |
+| `distance_per_stop` | 0.0094 |
 
 
 ## Usage
@@ -52,33 +64,19 @@ Workload Scoring Model
 ```python
 from skops import io as sio
 from huggingface_hub import hf_hub_download
+import numpy as np
 
-# Download and load
 model_path = hf_hub_download(repo_id="muthuk1/fairrelay-workload-scoring", filename="model.skops")
 untrusted = sio.get_untrusted_types(file=model_path)
 model = sio.load(model_path, trusted=untrusted)
 
-# Predict
-import numpy as np
-features = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
 prediction = model.predict(features)
 ```
 
-## Training Data
-
-- **Cainiao-AI/LaDe-D**: Real last-mile delivery data from Shanghai (KDD 2023)
-- **Africa Synth Last-Mile**: Synthetic Nigerian delivery data
-- **FairRelay Synthetic**: Physics-informed synthetic data calibrated to FairRelay's deterministic formulas
-
 ## Part of FairRelay
 
-FairRelay is an AI-powered logistics platform for fair load consolidation and dispatch:
-- 🚚 5-agent load consolidation pipeline (KMeans + OR-Tools CP-SAT)
-- ⚖️ 8-agent fair dispatch pipeline (Gini optimization)
-- 📊 XGBoost effort prediction + Thompson Sampling bandit
-- 🌱 EV-aware routing with battery constraints
-
-Built for **LogisticsNow Hackathon 2026** — Challenge #5: AI Load Consolidation
+FairRelay is an AI-powered logistics platform for fair load consolidation and dispatch.
+Built for **LogisticsNow Hackathon 2026** — Challenge #5: AI Load Consolidation.
 
 ## License