Nothing
r descr_models("linear_reg", "brulee")
defaults <- tibble::tibble(parsnip = c("penalty", "mixture"), default = c( "0.001", "0.0")) param <- linear_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 infomration 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).linear_reg(penalty = double(1)) %>% set_engine("brulee") %>% translate()
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