Step Forward Feature Selection: A Practical Example in Python

 By KDnuggets - 2021-03-21

Description

When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order ...

Summary

  • Step Forward Feature Selection: Step backward feature selection is closely related, and as you may have guessed starts with the entire set of features and works backward from there, removing features to find the optimal subset of a predefined size.
  • Keep in mind that an optimized set of selected features using a given algorithm may or may not perform equally well with a different algorithm.
  • Since we are more interested in demonstrating how to implement step forward feature selection than we are with the actual results on this particular dataset, we won't be overly concerned with the actual performance of our models, but we will compare the performances anyhow, as to show how it would be done in a meaningful project.

 

Topics

  1. Machine_Learning (0.17)
  2. NLP (0.13)
  3. Backend (0.11)

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