In topepo/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("logistic_reg", "keras")

Tuning Parameters

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

param <-
  logistic_reg() %>% 
  set_engine("keras") %>% 
  make_parameter_list(defaults)

This model has one tuning parameter:

param$item

For penalty, the amount of regularization is only L2 penalty (i.e., ridge or weight decay).

Translation from parsnip to the original package

logistic_reg(penalty = double(1)) %>% 
  set_engine("keras") %>% 
  translate()

[keras_mlp()] is a parsnip wrapper around keras code for neural networks. This model fits a linear regression as a network with a single hidden unit.

Preprocessing requirements

Case weights

Saving fitted model objects

Examples

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

References

• Hoerl, A., & Kennard, R. (2000). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 42(1), 80-86.

topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.