Lecture
Feature Selection
Description
This lecture covers the concept of feature selection, which involves reducing the number of features to an optimal subset. The instructor explains different approaches such as filtering and wrapping, and discusses the pitfalls of selecting features. Various methods like x2 statistics and information-theoretic approach are explored, along with examples of credibility features. The importance of feature normalization through standardization and scaling is also highlighted, with a comparison between the two techniques.
Official source
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