metadata
language: en
license: mit
library_name: scikit-learn
tags:
- tabular-classification
- legal-ai
- msme
- dispute-resolution
- lightgbm
- calibrated-probabilities
metrics:
- auc
- f1
- balanced_accuracy
model-index:
- name: MSME Payment Outcome Predictor (LightGBM)
results:
- task:
type: tabular-classification
dataset:
type: legal-disputes
name: MSME Payment Dispute Dataset
metrics:
- type: auc
value: 0.72
name: AUC
- type: f1
value: 0.61
name: F1 Score
- type: balanced_accuracy
value: 0.63
name: Balanced Accuracy
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