期刊名称：International Journal of Advanced Statistics and Probability
电子版ISSN：2307-9045
出版年度：2015
卷号：3
期号：1
页码：93-99
DOI：10.14419/ijasp.v3i1.4364
出版社：Journal of Advanced Computer Science & Technology
摘要：Some few decades ago, penalized regression techniques for linear regression have been developed specifically to reduce the flaws inherent in the prediction accuracy of the classical ordinary least squares (OLS) regression technique. In this paper, we used a diabetes data set obtained from previous literature to compare three of these well-known techniques, namely: Least Absolute Shrinkage Selection Operator (LASSO), Elastic Net and Correlation Adjusted Elastic Net (CAEN). After thorough analysis, it was observed that CAEN generated a less complex model.
