Reproducible Machine Learning for Credit Card Fraud detection - Practical handbook

Foreword

1. Book overview

1. Book content and intended audience
2. Book contributions
3. How to use this book

2. Background

1. Introduction
2. Credit card fraud scenarios
3. Credit card fraud detection system
4. Machine learning for credit card fraud detection
5. Summary

3. Getting started

1. Introduction
2. Transaction data simulator
3. Baseline feature transformation
4. Baseline fraud detection system
5. Real-world data
6. Summary

4. Performance metrics

1. Introduction
2. Threshold-based metrics
3. Threshold-free metrics
4. Precision top-k metrics
5. Real-world data
6. Summary

5. Model validation and model selection

1. Introduction
2. Validation strategies
3. Model selection
4. Real-world data
5. Summary

6. Imbalanced learning

1. Introduction
2. Cost-sensitive learning
3. Resampling strategies
4. Ensemble methods
5. Real-world data
6. Summary

7. Deep learning

1. Introduction
2. Feed-forward neural network
3. Autoencoders and anomaly detection
4. Sequential models and representation learning
5. Real-world data
6. Summary

References

1. Shared functions
2. Bibliography

Powered by Jupyter Book

Search

Searching for multiple words only shows matches that contain all words.

By Machine Learning Group (Université Libre de Bruxelles - ULB).

Code released under a GNU GPL v3.0 license. Prose and pictures released under a CC BY-SA 4.0 license.