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ACRIE
JenniferAnnKok/ACRIE
ACRIE is a machine learning model from JenniferAnnKok. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
An interactive credit scoring app built with XGBoost, SMOTE, and SHAP explainability — part of the Investec Portfolio Project.
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Updated May 23, 2026
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At a glance
- License
- mit
- Access
- Public
- Created
- May 23, 2026
- Updated
- May 23, 2026
- SHA
- b5137d6a
Model card
From the Hugging Face model README
🏦 Home Credit Default Risk Predictor
An interactive credit scoring app built with XGBoost, SMOTE, and SHAP explainability — part of the Investec Portfolio Project.
How it works
-
Fill in applicant details across the four input tabs:
- Application — income, loan amount, personal details
- Credit scores — external bureau scores (EXT_SOURCE_1/2/3)
- Bureau & history — bureau loan aggregates and previous applications
- Instalments — payment behaviour history
-
Click Predict default risk
-
The app returns:
- A probability gauge showing the estimated default probability
- A SHAP waterfall chart explaining which features drove the score
- A decision summary table (APPROVE / REJECT at the cost-optimal threshold)
Model details
| Property | Value |
|---|---|
| Algorithm | XGBoost (hist tree method) |
| Imbalance handling | SMOTE (sampling_strategy=0.3) |
| Hyperparameter search | RandomizedSearchCV (15 iter, 3-fold CV) |
| Evaluation metric | ROC-AUC |
| Target threshold | 0.27 (cost-optimal, not default 0.50) |
| Explainability | TreeSHAP via shap library |
Using your own trained model
Upload these three files (produced by the training pipeline) to the Space's file system:
home_credit_xgb_model.pkl ← joblib.dump(best_xgb, ...)
home_credit_threshold.pkl ← joblib.dump(cost_optimal_thresh, ...)
home_credit_features.pkl ← joblib.dump(X_train.columns.tolist(), ...)
The app detects them automatically on startup. If they are absent it falls back to a demo model trained on synthetic data so the Space stays functional.
Threshold logic
The cost-optimal threshold (default 0.27) minimises expected credit loss under:
- LGD = 45% of loan value (loss given default)
- Opportunity cost = 8% annual interest (rejected good client)
- False negatives are ~5× more costly than false positives at median loan size
Data sources (training)
| Table | Rows | Purpose |
|---|---|---|
application_train.csv | 307 511 | Main application features |
bureau.csv | 1 716 428 | External credit history |
previous_application.csv | 1 670 214 | Past Home Credit applications |
installments_payments.csv | 13 605 401 | Repayment behaviour (3M sampled) |
- License
- mit
- Created
- May 23, 2026
- Updated
- May 23, 2026