fairrelay-fairness-classifier

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

Part of the 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

Usage

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