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Novel Approach to Choosing Principal Components Number in Logistic Regression

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Zagreb, Croatia, 9-10 September 2021

Author

Listed:
  • Vrigazova, Borislava

Abstract

The confirmed approach to choosing the number of principal components for prediction models includes exploring the contribution of each principal component to the total variance of the target variable. A combination of possible important principal components can be chosen to explain a big part of the variance in the target. Sometimes several combinations of principal components should be explored to achieve the highest accuracy in classification. This research proposes a novel automatic way of deciding how many principal components should be retained to improve classification accuracy. We do that by combining principal components with the ANOVA selection. To improve the accuracy resulting from our automatic approach, we use the bootstrap procedure for model selection. We call this procedure the Bootstrapped-ANOVA PCA selection. Our results suggest that this procedure can automate the principal components selection and improve the accuracy of classification models, in our example, the logistic regression.

Suggested Citation

  • Vrigazova, Borislava, 2021. "Novel Approach to Choosing Principal Components Number in Logistic Regression," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2021), Hybrid Conference, Zagreb, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Hybrid Conference, Zagreb, Croatia, 9-10 September 2021, pages 1-12, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    • Handle: RePEc:zbw:entr21:262229
    DOI: 10.54820/PUCR5250
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    More about this item

    Keywords

    ANOVA; PCA; Bootstrap; logistic regression;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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