Nothing
r descr_models("multinom_reg", "brulee")
defaults <- tibble::tibble(parsnip = c("penalty", "mixture"), default = c( "0.001", "0.0")) param <- multinom_reg() %>% set_engine("brulee") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameter:
param$item
The use of the L1 penalty (a.k.a. the lasso penalty) does not force parameters to be strictly zero (as it does in packages such as glmnet). The zeroing out of parameters is a specific feature the optimization method used in those packages.
Other engine arguments of interest:
optimizer()
: The optimization method. See [brulee::brulee_linear_reg()].epochs()
: An integer for the number of passes through the training set. lean_rate()
: A number used to accelerate the gradient decsent process. momentum()
: A number used to use historical gradient information during optimization (optimizer = "SGD"
only).batch_size()
: An integer for the number of training set points in each batch.stop_iter()
: A non-negative integer for how many iterations with no improvement before stopping. (default: 5L).class_weights()
: Numeric class weights. See [brulee::brulee_multinomial_reg()].multinom_reg(penalty = double(1)) %>% set_engine("brulee") %>% translate()
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.