https://github.com/siam29/exploring-explainable-ai-demystifying-dt-rf-knn-xgbc

Implemented XAI techniques to enhance transparency in fraud detection models. I employed techniques such as SHAP, LIME on DT, RF, XGBC, and KNN to offer lucid explanations for transactions that were flagged.
https://github.com/siam29/exploring-explainable-ai-demystifying-dt-rf-knn-xgbc

machine-learning matplotlib pandas scikit-learn xai

Last synced: 29 days ago
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Implemented XAI techniques to enhance transparency in fraud detection models. I employed techniques such as SHAP, LIME on DT, RF, XGBC, and KNN to offer lucid explanations for transactions that were flagged.

• Host: GitHub
• URL: https://github.com/siam29/exploring-explainable-ai-demystifying-dt-rf-knn-xgbc
• Owner: siam29
• Created: 2024-07-28T15:53:36.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-07-28T15:54:40.000Z (5 months ago)
• Last Synced: 2024-12-06T04:16:51.167Z (29 days ago)
• Topics: machine-learning, matplotlib, pandas, scikit-learn, xai
• Language: Jupyter Notebook
• Homepage:
• Size: 2.01 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# XAI-on-DT-RF-XGBC-and-KNN

Implemented XAI techniques to enhance transparency in fraud detection models. I employed techniques such as SHAP, LIME on DT, RF, XGBC, and KNN to offer lucid explanations for transactions that were flagged.