I am working on a project where I am implementing Lasso regression in R for feature selection and my scenario is as follows.
For the minimum value of $\lambda$ most of the corresponding coefficients are zero (40 out 45 coefficients are zero). It is said that the coefficients will become zero when $\lambda$ is too high. On the contrary for me, the $\lambda$ value is very small (actually to the power of -5) i.e. I have a very small $\lambda$ value and most of the coefficients are zero.
So, I have a few questions listed below:
- Is this scenario common? Can I take any measures to prevent it?
- Is selecting by Lasso not suitable for feature selection in my scenario? If so, what are the other methods I can use?
Edit: Added R code below:
coef(cv.glmmod, s = "lambda.min")[which(coef(cv.glmmod, s = "lambda.min") != 0)] > 6.456279e-05 3.838600e-07 1.356334e-05 colnames(Final_raw)[which(coef(cv.glmmod, s = "lambda.min") != 0)] > "smart_1_raw" "smart_189_raw" "smart_198_raw" plot(cv.glmmod) [![enter image description here]] best_lambda <- cv.glmmod$lambda.min best_lambda > 9.175735e-05 (plot(cv.glmmod$glmnet.fit)) [![enter image description here]]
PS: I have also tried Ridge and Elastic net and the results were similar as the above.