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diabetes-predictor
Soulay/diabetes-predictor
diabetes-predictor is a machine learning model from Soulay. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for sklearn.
A Gradient Boosting Regressor trained on the sklearn Diabetes dataset to predict disease progression one year after baseline (continuous target).
Access
Public
Updated Mar 11, 2026
Repo size
252 KB
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0
Public
Repo makeup
Click a slice to open those files.
.joblib252 KB · 99%
At a glance
- Library
- sklearn
- Access
- Public
- Created
- Mar 11, 2026
- Updated
- Mar 11, 2026
- SHA
- 02bc6594
Model card
From the Hugging Face model README
Diabetes Disease Progression Predictor
A Gradient Boosting Regressor trained on the sklearn Diabetes dataset to predict disease progression one year after baseline (continuous target).
Results
| Metric | Value |
|---|---|
| Test MAE | 44.80 |
| Test R² | 0.4366 |
| CV R² (5-fold) | 0.3543 |
Feature Importances
| Feature | Importance |
|---|---|
| bmi | 0.3688 |
| s5 | 0.2360 |
| bp | 0.0869 |
| s2 | 0.0763 |
| age | 0.0559 |
| s6 | 0.0495 |
| s1 | 0.0474 |
| s3 | 0.0392 |
| s4 | 0.0277 |
| sex | 0.0122 |
Features
The model uses 10 baseline variables: age, sex, BMI, blood pressure, and 6 blood serum measurements (s1–s6).
Usage
import joblib
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="Soulay/diabetes-predictor", filename="model.joblib")
model = joblib.load(model_path)
# [age, sex, bmi, bp, s1, s2, s3, s4, s5, s6] (standardised values)
prediction = model.predict([[0.05, -0.04, 0.06, 0.02, -0.01, 0.0, -0.03, 0.04, 0.02, -0.01]])
print(prediction) # e.g. [152.3]
Training
Model trained automatically via GitHub Actions CI/CD on every push to main.
- Library
- sklearn
- Languages
- en
- Created
- Mar 11, 2026
- Updated
- Mar 11, 2026