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msme-payment-outcome-predictor-lightgbm
abhinavdread/msme-payment-outcome-predictor-lightgbm
msme-payment-outcome-predictor-lightgbm is a tabular classification model from abhinavdread. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for scikit-learn. The card lists the license as mit.
This model predicts the probabilistic outcome of MSME payment disputes:
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Updated Feb 20, 2026
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At a glance
- Task
- Tabular Classification
- Library
- scikit-learn
- License
- mit
- Access
- Public
- Created
- Feb 20, 2026
- Updated
- Feb 20, 2026
- SHA
- a0047d5e
Model card
From the Hugging Face model README
MSME Payment Outcome Predictor (LightGBM)
Overview
This model predicts the probabilistic outcome of MSME payment disputes:
- Win
- Settlement
- Escalation to MSEFC
The model outputs calibrated probabilities for each outcome.
Model Architecture
- Algorithm: LightGBM (Gradient Boosted Decision Trees)
- Calibration: Isotonic Regression (
CalibratedClassifierCV) - Preprocessing:
- OneHotEncoding (categorical features)
- Numeric features passthrough
- Class balancing enabled
Input Features
| Feature | Type |
|---|---|
| claim_amount | float |
| delay_days | float |
| buyer_type | categorical (govt/private) |
| contract_present | binary |
| industry_sector | categorical |
| claim_imputed | binary |
| delay_imputed | binary |
Output Format
{
"predicted_label": "win",
"probabilities": {
"win": 0.59,
"settlement": 0.05,
"escalation": 0.35
}
}
Performance Metrics
- Primary metric: AUC-ROC (macro) ≈ 0.72
- Balanced Accuracy ≈ 0.63
- F1 Macro ≈ 0.61
Intended Use
- Legal risk scoring
- MSME advisory tools
- Research prototype
- Decision support systems
Limitations
- Based on structured extracted data only
- Does not include full legal document text
- Not intended for judicial automation
- Task
- Tabular Classification
- Library
- scikit-learn
- License
- mit
- Languages
- en
- Created
- Feb 20, 2026
- Updated
- Feb 20, 2026