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1 results

  • 7 - Regularized Regression Methods

  • from Part II - Regression Methods for Estimation
  • Darius M. Dziuda, Central Connecticut State University
  • Book:
    Multivariate Biomarker Discovery
    Published online:
    30 May 2024
    Print publication:
    06 June 2024, pp 114-125

  • Summary

    Chapter 7 is dedicated to regularized regression methods, which – by penalizing models that are too complex – are capable of providing a reasonable tradeoff between bias and variance. Ridge regression implements L2 regularization, which results in more generalizable models, but does not perform any feature selection. L1 penalty used by the lasso allows, however, for simultaneous regularization and feature selection. The elastic net algorithm combines the two approaches by applying both L1 and L2 penalties, which allows for solutions combining the advantages of both ridge regression and the lasso. The chapter concludes by discussing a general class of Lq-regularized least squares optimization problems.