I did a regression on a train data set with 7000 observations and 50 explenatory variables with ols ridge and lasso. The lambda was chosen via cross validation. After that i wanted to compare the prediction accuracy of the 3 models by predict values of a test data set.
I thought i would get a better prediction with lasso and ridge but thats not the case.
How this could be ?What could be the possible reasons?
At the beginning i computed the vif for the ols model and had 3 variables where the vif was over 15. I thought when i have multicollinarity ridge and lasso will allways perform better ?