This project is part of Algorithm Audit's knowledge base.
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This project contains the data pipeline for training and testing a balanced random forest (BRF) model. This model is applied on:
- Kaggle - Credit Card Fraud Detection, 284.807 transactions with 0.172% fraud rate. Goal of this repository is to elaborate what sensitivity testing can be performed to strike a balance between precision and recall
- Balanced Random Forest (BRF)
- k-fold cross validation
- Precision-Recall
- Sensitivity testing