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1. Permutation Feature Importance from Scratch

   This article explains permutation feature importance (PFI), a popular method for understanding feature importance in explainable AI. The author walks through calculating PFI from scratch using Python and XGBoost, discussing the rationale behind the method and its limitations.

   2024-04-25 Tags: permutation, feature importance, pfi, explainable ai, xgboost, python, machine learning by klotz
2. Unlocking Insights: Random Forests for PCA and Feature Importance

   Discussion on the efficiency of Random Forest algorithms for PCA and Feature Importance. By Christopher Karg for Towards Data Science.

   2024-04-01 Tags: machine learning, feature importance, pca, random forests, towards data science by klotz
3. Random Forest for Feature Importance | by z_ai | Towards Data Science

   2021-03-12 Tags: machine learning, random forest, explainability, feature importance by klotz
4. How to Get Feature Importances from Any Sklearn Pipeline | by Nicolas Bertagnolli | Oct, 2020 | Towards Data Science

   2020-10-13 Tags: feature engineering, feature importance, machine learning, sklearn, python, pipeline by klotz

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