Performance Analysis of Regularized Linear Regression Models for Oxazolines and Oxazoles Derivatives Descriptor Dataset

Download Now
Provided by: Academy & Industry Research Collaboration Center
Topic: Big Data
Format: PDF
Regularized regression techniques for linear regression have been created the last few ten years to reduce the flaws of ordinary least squares regression with regard to prediction accuracy. In this paper, new methods for using regularized regression in model choice are introduced, and the authors distinguish the conditions in which regularized regression develops their ability to discriminate models. They applied all the five methods that use penalty-based (regularization) shrinkage to handle Oxazolines and Oxazoles derivatives descriptor dataset with far more predictors than observations.
Download Now

Find By Topic