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fairrelay-workload-scoring
muthuk1/fairrelay-workload-scoring
fairrelay-workload-scoring is a tabular regression model from muthuk1. Use it for the tabular regression task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as mit.
Part of the FairRelay AI logistics platform.
Access
Public
Updated Apr 24, 2026
Repo size
2.9 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.skops614 KB · 99%
At a glance
- Task
- Tabular Regression
- Library
- sklearn
- License
- mit
- Access
- Public
- Created
- Apr 24, 2026
- Updated
- Apr 24, 2026
- SHA
- 381338c6
Model card
From the Hugging Face model README
FairRelay — Workload Scoring Model (v2)
Part of the 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
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
- Task
- Tabular Regression
- Library
- sklearn
- License
- mit
- Created
- Apr 24, 2026
- Updated
- Apr 24, 2026