metadata
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 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
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