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

r descr_models("logistic_reg", "spark")

Tuning Parameters

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

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

This model has r nrow(param) tuning parameters:

param$item

For penalty, the amount of regularization includes both the L1 penalty (i.e., lasso) and the L2 penalty (i.e., ridge or weight decay). 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

logistic_reg(penalty = double(1), mixture = double(1)) %>% 
  set_engine("spark") %>% 
  translate()

Preprocessing requirements

By default, ml_logistic_regression() uses the argument standardization = TRUE to center and scale the data.

Case weights

Note that, for spark engines, the case_weight argument value should be a character string to specify the column with the numeric case weights.

Other details

References

Luraschi, J, K Kuo, and E Ruiz. 2019. Mastering Spark with R. O'Reilly Media
Hastie, T, R Tibshirani, and M Wainwright. 2015. Statistical Learning with Sparsity. CRC Press.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.