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1. 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
2. Python MANOVA Made Easy using Statsmodels - Erik Marsja

   2021-05-16 Tags: manova, python, statistics, root cause analysis, feature importance 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
5. Solved: Splunk Machine Learning Toolkit: How do I determin... - Splunk Community

   Cool question - and yes, you're right that you can use the summary command to inspect feature_importances for some of the models (e.g. RandomForestClassifier). Other models may not support the same type of summary however.
   
   You should also check out the FieldSelector algorithm which is really useful for this problem. Under the hood, it uses ANOVA & F-Tests to estimate the linear dependency between variables. Although its univariate (not capturing any interactions between variables), it still can provide a good baseline from choosing a handful of features from hundreds.

   2020-09-14 Tags: splunk, mltk, feature engineering, feature importance, model by klotz

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