8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics

Feature selection is hard! So, I explain how you can use a combination of variable clustering and feature importance to help create a shortlist. I will also explain the key factors you need to consider when selecting features. The most important are predictive power and predictor variety. But there are also other considerations including data quality and availability, feature stability, interpretability and law or ethics. We end by discussing how all these considerations come together in a feature selection framework.
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🚀 Chapters 🚀
00:00 Introduction
01:37 What is feature selection?
02:54 Redundant & irrelevant features
04:42 Predictive power & predictor variety
07:37 Variable clustering
09:23: Other characteristics & considerations

[@adataodyssey]

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Signup here: mailchi.mp/40909011987b/signup
XAI course: adataodyssey.com/courses/xai-with-python/
SHAP course: adataodyssey.com/courses/shap-with-python/

[@ShivSingh-zv1xw]

I have recently joined a course on eXplainable Artificial Intelligence (XAI) of yours and I am interested in applying the concepts of interpretability to image data while ensuring that the model's accuracy is preserved. please do create some videos on that topic. Thank you!