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TUTCRIS

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut359-383
JulkaisuMachine Learning and Knowledge Extraction
Vuosikerta1
Numero1
DOI - pysyväislinkit
TilaJulkaistu - 14 tammikuuta 2019
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.

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