README.md

---
library_name: sklearn
tags:
  - fairrelay
  - logistics
  - xgboost
  - sklearn
  - tabular-classification
  - fairness
datasets:
  - Cainiao-AI/LaDe-D
license: mit
---

# FairRelay — Fairness Classification Model (ACCEPT vs REOPTIMIZE) (v2)

Part of the **[FairRelay](https://github.com/MUTHUKUMARAN-K-1/FairRelay)** AI logistics platform.

## Model Description

Fairness Classification Model (ACCEPT vs REOPTIMIZE)

**Version**: v2 — Retrained with realistic, harder data to prevent overfitting and improve real-world robustness.

**Type**: XGBoost Pipeline (StandardScaler + XGBoost)
**Task**: Classification

### 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

- **Accuracy**: 0.9000
- **F1 Score**: 0.9369
- **Precision**: 0.9266
- **Recall**: 0.9474
- **Train-Test Gap**: 0.0211
- **CV F1 (5-fold)**: 0.9401 ± 0.0012


## Input Features

| Feature | Importance |
|---------|-----------|
| `num_drivers` | 0.0255 |
| `avg_effort` | 0.0151 |
| `std_dev` | 0.1706 |
| `max_gap` | 0.5543 |
| `gini_index` | 0.0585 |
| `min_effort` | 0.0152 |
| `max_effort` | 0.0209 |
| `outlier_count` | 0.0605 |
| `pct_above_avg` | 0.0138 |
| `effort_cv` | 0.0334 |
| `skewness` | 0.0145 |
| `kurtosis` | 0.0176 |


## 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-fairness-classifier", filename="model.skops")
untrusted = sio.get_untrusted_types(file=model_path)
model = sio.load(model_path, trusted=untrusted)

prediction = model.predict(features)
```

## Part of FairRelay

FairRelay is an AI-powered logistics platform for fair load consolidation and dispatch.
Built for **LogisticsNow Hackathon 2026** — Challenge #5: AI Load Consolidation.

## License

MIT