File size: 2,306 Bytes
a0047d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | ---
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
```json
{
"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 |