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fraud-detection-model
votrananhquan/fraud-detection-model
fraud-detection-model is a machine learning model from votrananhquan. 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.
Champion model for credit card fraud detection on the Kaggle Credit Card Fraud Dataset. Registered in MLflow Registry as fraud-detection-model@production.
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Updated Sep 11, 2026
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At a glance
- License
- mit
- Access
- Public
- Created
- Sep 11, 2026
- Updated
- Sep 11, 2026
- SHA
- 3eff7729
Model card
From the Hugging Face model README
Credit Card Fraud Detection — Champion Model
Model Description
Champion model for credit card fraud detection on the
Kaggle Credit Card Fraud Dataset.
Registered in MLflow Registry as fraud-detection-model@production.
Trained under a leak-free protocol: stratified 64/16/20 train/validation/test split, the
Amount scaler fitted on the training split only, early stopping watched on validation, and
the champion selected by validation PR-AUC. The test split influences no decision and is
scored once for reporting.
Performance
Selected on validation (n/a rows):
| Metric | Value |
|---|---|
| PR-AUC | 0.7407 |
| Recall | 0.8354 |
| Precision | 0.5238 |
| F1 | 0.6439 |
Reported on the held-out test split (n/a rows):
| Metric | Value |
|---|---|
| PR-AUC | 0.7462 |
| Recall | 0.8878 |
| Precision | 0.4555 |
| F1 | 0.6021 |
| True positives | 87 |
| False positives | 104 |
| False negatives | 11 |
Usage
import joblib
import numpy as np
model = joblib.load("baseline_lr.pkl")
# features: V1-V28 (PCA, pass through), Amount scaled with the TRAINING-split
# StandardScaler: mu=87.9702, sigma=245.5762
X = np.array([[...]]) # shape (1, 29)
proba = model.predict_proba(X)[:, 1] # fraud probability
MLflow Tracking
- Run ID:
176f185e3b7a400dba35d6f841de31e2 - Model version:
3 - Registered name:
fraud-detection-model
- License
- mit
- Languages
- en
- Created
- Sep 11, 2026
- Updated
- Sep 11, 2026