In lasso or ridge regression, one has to specify a shrinkage parameter, often called by \lambda or \alpha. This value is often chosen via cross validation by checking a bunch of different values on training data and seeing which yields the best e.g. R^2 on test data. What is the range of values one should check? Is it (0,1)?

**Answer**

You don’t really need to bother. In most packages (like glmnet) if you do not specify \lambda, the software package generates its own sequence (which is often recommended). The reason I stress this answer is that during the running of the LASSO the solver generates a sequence of \lambda, so while it may counterintuitive providing a single \lambda value may actually slow the solver down considerably (When you provide an exact parameter the solver resorts to solving a semi definite program which can be slow for reasonably ‘simple’ cases.)

As for the exact value of \lambda you can potentially chose whatever you want from [0,\infty[. Note that if your \lambda value is too large the penalty will be too large and hence none of the coefficients can be non-zero. If the penalty is too small you will overfit the model and this will not be the best cross validated solution

**Attribution***Source : Link , Question Author : rhombidodecahedron , Answer Author : Sid*