| --- |
| 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 |
