Register Encoding Options for Model — set

This function is used to register encoding information for a model, engine, and mode combination.

Usage

set_encoding(model, mode, eng, options)

get_encoding(model)

Arguments

model: A single character string for the model type (e.g. "k_means", etc).
mode: A single character string for the model mode (e.g. "partition").
eng: A single character string for the model engine.
options: A list of options for engine-specific preprocessing encodings. See Details below.

Value

A tibble

Details

The list passed to options needs the following values:

predictor_indicators describes whether and how to create indicator/dummy variables from factor predictors. There are three options: "none" (do not expand factor predictors), "traditional" (apply the standard model.matrix() encodings), and "one_hot" (create the complete set including the baseline level for all factors).
compute_intercept controls whether model.matrix() should include the intercept in its formula. This affects more than the inclusion of an intercept column. With an intercept, model.matrix() computes dummy variables for all but one factor level. Without an intercept, model.matrix() computes a full set of indicators for the first factor variable, but an incomplete set for the remainder.
remove_intercept removes the intercept column after model.matrix() is finished. This can be useful if the model function (e.g. lm()) automatically generates an intercept.
allow_sparse_x specifies whether the model can accommodate a sparse representation for predictors during fitting and tuning.

Examples

if (FALSE) {
set_new_model("shallow_learning_model")
set_model_mode("shallow_learning_model", "partition")
set_model_engine("shallow_learning_model", "partition", "stats")

set_encoding(
  model = "shallow_learning_model",
  mode = "partition",
  eng = "stats",
  options = list(
    predictor_indicators = "traditional",
    compute_intercept = TRUE,
    remove_intercept = TRUE,
    allow_sparse_x = FALSE
  )
)

get_encoding("shallow_learning_model")
get_encoding("shallow_learning_model")$value
}