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On the Relationship between Feature Selection Metrics and Accuracy.

Elise Epstein1, Naren Nallapareddy1, Soumya Ray1

  • 1Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA.

Entropy (Basel, Switzerland)
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

Feature selection metrics can rank features differently than model accuracy. This study analyzes "misordering" of features, finding systematic differences among metrics and confirming this occurs in real-world machine learning datasets.

Keywords:
decision treesfeature selectionmodel selection

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Area of Science:

  • Machine Learning
  • Data Science
  • Statistical Modeling

Background:

  • Feature selection is crucial for building effective predictive models.
  • Commonly used metrics for feature selection are often evaluated based on final model accuracy.
  • A discrepancy can arise if feature selection metrics do not align with accuracy-based rankings.

Purpose of the Study:

  • To investigate the relationship between common feature selection metrics and predictive model accuracy.
  • To analyze the phenomenon of 'misordering', where feature selection metrics rank features differently than accuracy.
  • To understand the frequency and conditions under which misordering occurs.

Main Methods:

  • Analytical investigation of feature selection metric 'misordering' based on data partitioning parameters.
  • Empirical evaluation of various feature selection metrics on real-world datasets.
  • Comparison of analytical predictions with empirical observations of misordering.

Main Results:

  • Different feature selection metrics exhibit systematic differences in their likelihood of causing misordering.
  • Misordering can occur across a wide range of data partitioning parameters.
  • Empirical results on real-world datasets largely align with analytical predictions regarding misordering frequency.

Conclusions:

  • Feature selection metrics do not always align with accuracy-based feature rankings.
  • Understanding misordering provides insight into the practical performance and limitations of different feature selection metrics.
  • The study highlights the importance of considering metric-specific behavior in machine learning pipelines.