I have 500 observations and 200 predictors, and I want to do the prediction while selecting some important features. I know that regularisation method (ridge, lasso) are shrinkage method for high-dimensional setting, and the lasso also has an advantage of model selection. It is the fact that the setting with 500 observations and 200 predictors is not a high-dimensional setting, but I want to do some feature selection since I have 200 features. Can I use lasso when it is not a high dimensional setting?
Solved – use lasso when it is not a high dimensional setting
feature selectionhigh-dimensionallassoregularizationridge regression
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Best Answer
There's nothing that suggests you need a number of predictors ($p$) as large as 200 or sample size ($n$) as large 500, let alone larger. (You might find it surprising to read some of the early papers on both methods.)
You can very successfully use regularization methods like ridge regression and lasso on problems with only a few predictors -- the benefits of regularization are still present (indeed the illustration here shows ridge regression can be useful with two predictors, and one can make an argument for considering it even with a single predictor.)