In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("poisson_reg", "glmnet")

Tuning Parameters

defaults <- 
  tibble::tibble(parsnip = c("penalty", "mixture"),
                 default = c("see below", "1.0"))

param <-
poisson_reg() %>% 
  set_engine("glmnet") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

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]. As for mixture:

• mixture = 1 specifies a pure lasso model,
• mixture = 0 specifies a ridge regression model, and
• 0 < mixture < 1 specifies an elastic net model, interpolating lasso and ridge.

Translation from parsnip to the original package

r uses_extension("poisson_reg", "glmnet", "regression")

library(poissonreg)

poisson_reg(penalty = double(1), mixture = double(1)) %>% 
  set_engine("glmnet") %>% 
  translate()

Preprocessing requirements

By default, glmnet::glmnet() uses the argument standardize = TRUE to center and scale the data.

Case weights

Saving fitted model objects

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.