Improving Prediction Accuracy of Lasso and Ridge Regression as an Alternative to LS Regression to Identify Variable Selection Problems

This paper introduces the Lasso and Ridge Regression methods, which are two popular regularization approaches. The method they give a penalty to the coefficients differs in both of them. L1 Regularization refers to Lasso linear regression, while L2 Regularization refers to Ridge regression. As we all know, regression models serve two main purposes: explanation and prediction of scientific phenomena. Where prediction accuracy will be optimized by balancing each of the bias and variance of predictions, while explanation will be gained by constructing interpretable regression models by variable selection. The penalized regression method, also known as Lasso regression, adds bias to the model's estimates and reduces variance to enhance prediction. Ridge regression, on the other hand, introduces a minor amount of bias in the data to get long-term predictions. In the presence of multicollinearity, both regression methods have been offered as an alternative to the least square approach (LS). Because they deal with multicollinearity, they have the appropriate properties to reduce numerical instability caused by overfitting. As a result, prediction accuracy can be improved. For this study, the Corona virus disease (Covid-19) dataset was used, which has had a significant impact on global life. Particularly in our region (Kurdistan), where life has altered dramatically and many people have succumbed to this deadly sickness. Our data is utilized to analyze the benefits of each of the two regression methods. The results show that the Lasso approach produces more accurate and dependable or reliable results in the presence of multicollinearity than Ridge and LS methods when compared in terms of accuracy of predictions by using NCSS10, EViews 12 and SPSS 25.