I have a set of 150 features, and many of them are highly correlated with each other. My goal is to predict the value of a discrete variable, whose range is 1-8. My sample size is 550, and I am using 10-fold cross-validation.

AFAIK, among the regularization methods (Lasso, ElasticNet, and Ridge), Ridge is more rigorous to correlation among the features. That is way I expected that with Ridge, I should obtain a more accurate prediction. However, my results show that the mean absolute error of Lasso or Elastic is around 0.61 whereas this score is 0.97 for the ridge regression. I wonder what would be an explanation for this. Is this because I have many features, and Lasso performs better because it makes a sort of feature selection, getting rid of the redundant features?

I have a set of 150 features, and many of them are highly correlated with each other. My goal is to predict the value of a discrete variable, whose range is 1-8. My sample size is 550, and I am using 10-fold cross-validation.

AFAIK, among the regularization methods (Lasso, ElasticNet, and Ridge), Ridge is more rigorous to correlation among the features. That is why I expected that with Ridge, I should obtain a more accurate prediction. However, my results show that the mean absolute error of Lasso or Elastic is around 0.61 whereas this score is 0.97 for the ridge regression. I wonder what would be an explanation for this. Is this because I have many features, and Lasso performs better because it makes a sort of feature selection, getting rid of the redundant features?

I have a set of 150 features, and many of them are highly correlated with each other. My goal is to predict the value of a discrete variable, whose range is 1-8. AFAIK, among the regularization methods (Lasso, ElasticNet, and Ridge), Ridge is more rigorous to regression. That is way I expected that with Ridge, I should obtain a more accurate prediction. However, my results show that the mean absolute error of Lasso or Elastic is around 0.61 whereas this score is 0.97 for the ridge regression. I wonder what would be an explanation for this. Is this because I have many features, and Lasso performs better because it makes a sort of feature selection, getting rid of the redundant features?

I have a set of 150 features, and many of them are highly correlated with each other. My goal is to predict the value of a discrete variable, whose range is 1-8. My sample size is 550, and I am using 10-fold cross-validation. 

AFAIK, among the regularization methods (Lasso, ElasticNet, and Ridge), Ridge is more rigorous to correlation among the features. That is way I expected that with Ridge, I should obtain a more accurate prediction. However, my results show that the mean absolute error of Lasso or Elastic is around 0.61 whereas this score is 0.97 for the ridge regression. I wonder what would be an explanation for this. Is this because I have many features, and Lasso performs better because it makes a sort of feature selection, getting rid of the redundant features?