A comprehensive study of feature selection techniques in machine learning models

HIGHLIGHTS

• What: The paper discusses the applications of feature selection across various domains including healthcare finance and image processing and examines how metrics such as accuracy precision and recall can assess the effectiveness of feature selection. By systematically exploring the various approaches-filter, wrapper, and embedded methods-the paper seeks to elucidate their strengths, limitations, and suitability for different data contexts. The aim of feature selection is to ensure that machine_learning models focus on the most informative variables, leading to better generalization and predictive accuracy. The feature selection process is applied to each fold, and the model is . . .
  

   

  

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