Summary of Impact Of Sampling Techniques and Data Leakage on Xgboost Performance in Credit Card Fraud Detection, by Siyaxolisa Kabane
Impact of Sampling Techniques and Data Leakage on XGBoost Performance in Credit Card Fraud Detection
by Siyaxolisa Kabane
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the pressing issue of credit card fraud detection using machine learning models like XGBoost. The problem is exacerbated by class imbalances in transaction datasets, which can affect model performance. To combat this, researchers often employ sampling techniques to address the imbalance. However, these techniques are typically implemented before the train-test split, risking data leakage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Credit card fraud detection is a big deal in financial security! Machine learning models like XGBoost help identify fraudulent transactions. But, there’s a problem – most transaction datasets have more good transactions than bad ones, making it harder for models to work well. To fix this, people use special techniques to balance the data. The question is, when do you apply these techniques? If you do it before splitting your data into training and testing sets, you might accidentally introduce errors. |
Keywords
» Artificial intelligence » Machine learning » Xgboost
