r descr_models("linear_reg", "glmnet")
defaults <- tibble::tibble(parsnip = c("penalty", "mixture"), default = c("see below", "1.0")) param <- linear_reg() %>% set_engine("glmnet") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameters:
param$item
A value of mixture = 1
corresponds to a pure lasso model, while mixture = 0
indicates ridge regression.
The penalty
parameter has no default and requires a single numeric value. For more details about this, and the glmnet
model in general, see [glmnet-details].
linear_reg(penalty = double(1), mixture = double(1)) %>% set_engine("glmnet") %>% translate()
By default, [glmnet::glmnet()] uses the argument standardize = TRUE
to center and scale the data.
The "Fitting and Predicting with parsnip" article contains examples for linear_reg()
with the "glmnet"
engine.
Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.