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How to use Caret and glmnet in R?
I want to select variables with good discriminant and predictive performance. So I decided to use caret and glmnet R packages as described here : https://quantmacro.wordpress.com/2016/04/26/fitting-elastic-net-model-in-r/ The “bestTune” parameter are alpha = 0.1 and lambda = 100.
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.
How to fitting elastic net model in R?
So I decided to use caret and glmnet R packages as described here : https://quantmacro.wordpress.com/2016/04/26/fitting-elastic-net-model-in-r/ The “bestTune” parameter are alpha = 0.1 and lambda = 100. When I run again with a larger lambda.grid, it’s always the max of lambda which is given in the output…
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.
When to use a variable selection in glmnet?
After the variable selection is done i would use those variables for predictions with glmnet as well as for a linear model. What you’re trying to do here is to identify most “important” variables within glmnet and then trying to pass your features to another model.
How does feature selection work in caret package?
Built-in feature selection typically couples the predictor search algorithm with the parameter estimation and are usually optimized with a single objective function (e.g. error rates or likelihood). From theoretical perspective: different models allow for different degree of flexibility and protection against overfit.
Which is an example of feature elimination in caret?
20Recursive Feature Elimination 20.1Backwards Selection 20.2Resampling and External Validation 20.3Recursive Feature Elimination via caret 20.4An Example