Compared the mean square error and R-Square value using linear model, Lasso and ridge regression
Compared the mean square error and R-Square value using linear model, Lasso and ridge regression.
Lasso -In statistics and machine learning, lasso (least absolute shrinkage and selection operator) (also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces.
Ridge-Ridge Regression is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value.
Objective of this comparison is to find out how this techniques works and where we can used it.
Findings:
It is observed that if the input variables is many than than Lasso is useful wherese in case of multicollinearity Ridge might work better.