How to pick the best Lambda in glmnet?
You need to pick a “best” lambda, and lambda.1se is a reasonable, or justifiable, one to pick. But you could use cv.glmnet (as.matrix (mtcars [-1]), mtcars [,1])$lambda.min or any other value of lambda that you settle upon as “best” for you. To extract the optimal lambda, you could type fit$lambda.min
Which is the best value for lambda.min?
You need to pick a “best” lambda, and lambda.1se is a reasonable, or justifiable, one to pick. But you could use cv.glmnet (as.matrix (mtcars [-1]), mtcars [,1])$lambda.min or any other value of lambda that you settle upon as “best” for you.
What are the special values of λ in glmnet?
Two special values along the λ sequence are indicated by the vertical dotted lines. lambda.min is the value of λ that gives minimum mean cross-validated error, while lambda.1se is the value of λ that gives the most regularized model such that the cross-validated error is within one standard error of the minimum.
How is the regularization path computed in glmnet?
Glmnet is a package that fits generalized linear and similar models via penalized maximum likelihood. The regularization path is computed for the lasso or elastic net penalty at a grid of values (on the log scale) for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix x.
What kind of regression models can glmnet fit?
The algorithm is extremely fast, and can exploit sparsity in the input matrix x. It fits linear, logistic and multinomial, poisson, and Cox regression models. It can also fit multi-response linear regression, generalized linear models for custom families, and relaxed lasso regression models.
How does glmnet return a sequence of models?
The function glmnet returns a sequence of models for the users to choose from. In many cases, users may prefer the software to select one of them.