Add model card for Fairness Classification Model (ACCEPT vs REOPTIMIZE)
Browse files
README.md
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---
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library_name: sklearn
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tags:
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- tabular-classification
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- logistics
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- xgboost
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- sklearn
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- fairness
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- gini-index
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- fairrelay
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datasets:
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- Cainiao-AI/LaDe-D
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- electricsheepafrica/africa-synth-retail-and-ecommerce-last-mile-delivery-data-nigeria
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license: mit
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---
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# FairRelay — Fairness Classification Model (ACCEPT vs REOPTIMIZE)
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Part of the **[FairRelay](https://github.com/MUTHUKUMARAN-K-1/FairRelay)** AI logistics platform.
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## Model Description
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Fairness Classification Model (ACCEPT vs REOPTIMIZE)
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**Type**: XGBClassifier Pipeline
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**Framework**: scikit-learn Pipeline + XGBoost
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**Task**: Classification
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## Performance
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- **Accuracy**: 0.9992
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- **F1 Score**: 0.9994
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## Input Features
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| Feature | Importance |
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|---------|-----------|
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| `num_drivers` | 0.0280 |
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| `avg_effort` | 0.0012 |
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| `std_dev` | 0.2209 |
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| `max_gap` | 0.7071 |
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| `gini_index` | 0.0096 |
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| `min_effort` | 0.0022 |
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| `max_effort` | 0.0012 |
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| `outlier_count` | 0.0179 |
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| `pct_above_avg` | 0.0013 |
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| `effort_cv` | 0.0106 |
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## Usage
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```python
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from skops import io as sio
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from huggingface_hub import hf_hub_download
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# Download and load
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model_path = hf_hub_download(repo_id="muthuk1/fairrelay-fairness-classifier", filename="model.skops")
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untrusted = sio.get_untrusted_types(file=model_path)
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model = sio.load(model_path, trusted=untrusted)
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# Predict
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import numpy as np
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features = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
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prediction = model.predict(features)
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```
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## Training Data
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- **Cainiao-AI/LaDe-D**: Real last-mile delivery data from Shanghai (KDD 2023)
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- **Africa Synth Last-Mile**: Synthetic Nigerian delivery data
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- **FairRelay Synthetic**: Physics-informed synthetic data calibrated to FairRelay's deterministic formulas
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## Part of FairRelay
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FairRelay is an AI-powered logistics platform for fair load consolidation and dispatch:
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- 🚚 5-agent load consolidation pipeline (KMeans + OR-Tools CP-SAT)
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- ⚖️ 8-agent fair dispatch pipeline (Gini optimization)
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- 📊 XGBoost effort prediction + Thompson Sampling bandit
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- 🌱 EV-aware routing with battery constraints
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Built for **LogisticsNow Hackathon 2026** — Challenge #5: AI Load Consolidation
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## License
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MIT
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