On the Prediction Accuracies of Three Most Known Regularizers : Ridge Regression, The Lasso Estimate and Elastic Net Regularization Methods

Abstract

The work in this paper shows intensive empirical experiments using 13 datasets to understand the regularization effectiveness of ridge regression, the lasso estimate, and elastic net regularization methods. The study offers a deep understanding of how the datasets affect the goodness of the prediction accuracy of each regularization method for a given problem given the diversity in the datasets used. The results have shown that datasets play crucial rules on the performance of the regularization method and that the predication accuracy depends heavily on the nature of the sampled datasets.