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klotz: explainability* + shapley functions*

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1. Explaining Anomalies with Isolation Forest and SHAP

   This article explores the use of Isolation Forest for anomaly detection and how SHAP (KernelSHAP and TreeSHAP) can be applied to explain the anomalies detected, providing insights into which features contribute to anomaly scores.

   2024-09-30 Tags: isolation forest, shap, anomaly detection, explainability, data science by klotz
2. What If We Could Easily Explain Overly Complex Models?

   Generating counterfactual explanations got a lot easier with CFNOW, but what are counterfactual explanations, and how can I use them?

   2023-09-30 Tags: machine learning, explainability, feature selection, lime, shap, counterfactual, cfnow, xai by klotz
3. Shap

   2023-01-28 Tags: shapley functions, shap, explainability, machine learning by klotz
4. Introduction to SHAP Values and their Application in Machine Learning | by Reza Bagheri | Mar, 2022 | Towards Data Science

   2022-04-18 Tags: shap, machine learning, deep learning, explainability by klotz
5. 7 Best Python Packages Kagglers Are Using Without Telling You | Towards Data Science

   2021-08-09 Tags: data science, data engineering, exploratory data analysis, shap, explainability, machine learning, jupyter, python, training, ensemble by klotz
6. Shapash makes Machine Learning models understandable by everyone | Towards AI

   2021-03-17 Tags: shapash, explainability, shap, lime, machine learning, svm by klotz
7. Can SHAP trigger a paradigm shift in Risk Analytics? | by Chandan Durgia | Jan, 2021 | Towards Data Science

   2021-01-11 Tags: shap, explainability, machine_learning, risk, analytics, medium by klotz
8. Demystify your ML model with SHAP | by Yifei Huang | Dec, 2020 | Towards Data Science

   2021-01-03 Tags: shap, machine learning, explainability, root cause analysis by klotz
9. LIME vs. SHAP: Which is Better for Explaining Machine Learning Models? | by Dario Radečić | Dec, 2020 | Towards Data Science

   2020-12-15 Tags: lime, shap, explainability, machine learning by klotz
10. SHAP: How to Interpret Machine Learning Models With Python | by Dario Radečić | Nov, 2020 | Towards Data Science

    2020-11-10 Tags: shap, explainability, machine learning, tutorial, python by klotz

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