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library_name: sklearn
tags:
- fairrelay
- logistics
- xgboost
- sklearn
- tabular-regression
- workload
- route-optimization
license: mit
---
# FairRelay — Workload Scoring Model (v2)
Part of the **[FairRelay](https://github.com/MUTHUKUMARAN-K-1/FairRelay)** AI logistics platform.
## Model Description
Predicts delivery route workload score based on package count, weight, stops, distance, difficulty, and fragility. The workload score quantifies how demanding a route is for a driver.
**Version**: v2 — Retrained with realistic data including hidden confounders, heteroscedastic noise, non-linear interactions, and measurement error. Properly regularized to prevent overfitting.
**Type**: XGBRegressor Pipeline (StandardScaler + XGBoost)
**Task**: Regression
### v2 vs v1
| Metric | v1 | v2 |
|--------|----|----|
| Test R² | 0.9969 (suspiciously high) | **0.7577** (realistic) |
| Train-Test Gap | 0.0010 | **0.0156** |
| Why | Clean formula + 5% noise | Hidden confounders, noise, interactions |
## Performance
- **R²**: 0.7577
- **MAE**: 66.13
- **RMSE**: 86.85
- **Train-Test R² Gap**: 0.0156 (no overfitting)
- **CV R² (5-fold)**: 0.7614 ± 0.0036
## Input Features
| Feature | Importance |
|---------|-----------|
| `num_packages` | 0.1573 |
| `total_weight_kg` | 0.0183 |
| `num_stops` | 0.4728 |
| `avg_fragility` | 0.0110 |
| `total_distance_km` | 0.0080 |
| `route_difficulty_score` | 0.2582 |
| `estimated_time_minutes` | 0.0420 |
| `packages_per_stop` | 0.0212 |
| `weight_per_package` | 0.0069 |
| `distance_per_stop` | 0.0044 |
## Usage
```python
from skops import io as sio
from huggingface_hub import hf_hub_download
import numpy as np
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)
# [num_packages, total_weight_kg, num_stops, avg_fragility, total_distance_km,
# route_difficulty_score, estimated_time_minutes, packages_per_stop,
# weight_per_package, distance_per_stop]
features = np.array([[25, 50.0, 15, 2.5, 12.0, 10.5, 120.0, 1.67, 2.0, 0.8]])
workload = model.predict(features)
print(f"Workload score: {workload[0]:.1f}")
```
## License
MIT
|