In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("logistic_reg", "brulee")

Tuning Parameters

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defaults <- 
  tibble::tibble(parsnip = c("penalty", "mixture"),
                 default = c(  "0.001", "0.0"))

param <-
  logistic_reg() |> 
  set_engine("brulee") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameter:

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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_logistic_reg()].

Translation from parsnip to the original package (classification)

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logistic_reg(penalty = double(1)) |> 
  set_engine("brulee") |> 
  translate()

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Case weights

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References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

parsnip documentation built on June 8, 2025, 12:10 p.m.