In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("logistic_reg", "glmnet")

Tuning Parameters

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

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

This model has r nrow(param) tuning parameters:

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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

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logistic_reg(penalty = double(1), mixture = double(1)) |> 
  set_engine("glmnet") |> 
  translate()

Preprocessing requirements

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

Case weights

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Sparse Data

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Saving fitted model objects

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Examples

The "Fitting and Predicting with parsnip" article contains examples for logistic_reg() with the "glmnet" engine.

References

Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.