new-default-blueprint: Create a new default blueprint
In tidymodels/hardhat: Construct Modeling Packages

new_default_formula_blueprint

R Documentation

Create a new default blueprint

Description

This page contains the constructors for the default blueprints. They can be extended if you want to add extra behavior on top of what the default blueprints already do, but generally you will extend the non-default versions of the constructors found in the documentation for new_blueprint().

Usage

new_default_formula_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  ptypes = NULL,
  formula = NULL,
  indicators = "traditional",
  composition = "tibble",
  terms = list(predictors = NULL, outcomes = NULL),
  levels = NULL,
  ...,
  subclass = character()
)

new_default_recipe_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  fresh = TRUE,
  strings_as_factors = TRUE,
  composition = "tibble",
  ptypes = NULL,
  recipe = NULL,
  extra_role_ptypes = NULL,
  ...,
  subclass = character()
)

new_default_xy_blueprint(
  intercept = FALSE,
  allow_novel_levels = FALSE,
  composition = "tibble",
  ptypes = NULL,
  ...,
  subclass = character()
)

Arguments

`intercept`	A logical. Should an intercept be included in the processed data? This information is used by the `process` function in the `mold` and `forge` function list.
`allow_novel_levels`	A logical. Should novel factor levels be allowed at prediction time? This information is used by the `clean` function in the `forge` function list, and is passed on to `scream()`.
`ptypes`	Either `NULL`, or a named list with 2 elements, `predictors` and `outcomes`, both of which are 0-row tibbles. `ptypes` is generated automatically at `mold()` time and is used to validate `new_data` at prediction time.
`formula`	Either `NULL`, or a formula that specifies how the predictors and outcomes should be preprocessed. This argument is set automatically at `mold()` time.
`indicators`	A single character string. Control how factors are expanded into dummy variable indicator columns. One of: `"traditional"` - The default. Create dummy variables using the traditional `model.matrix()` infrastructure. Generally this creates `K - 1` indicator columns for each factor, where `K` is the number of levels in that factor. `"none"` - Leave factor variables alone. No expansion is done. `"one_hot"` - Create dummy variables using a one-hot encoding approach that expands unordered factors into all `K` indicator columns, rather than `K - 1`.
`composition`	Either "tibble", "matrix", or "dgCMatrix" for the format of the processed predictors. If "matrix" or "dgCMatrix" are chosen, all of the predictors must be numeric after the preprocessing method has been applied; otherwise an error is thrown.
`terms`	A named list of two elements, `predictors` and `outcomes`. Both elements are `terms` objects that describe the terms for the outcomes and predictors separately. This argument is set automatically at `mold()` time.
`levels`	Either `NULL` or a named list of character vectors that correspond to the levels observed when converting character predictor columns to factors during `mold()`. This argument is set automatically at `mold()` time.
`...`	Name-value pairs for additional elements of blueprints that subclass this blueprint.
`subclass`	A character vector. The subclasses of this blueprint.
`fresh`	Should already trained operations be re-trained when `prep()` is called?
`strings_as_factors`	Should character columns be converted to factors when `prep()` is called?
`recipe`	Either `NULL`, or an unprepped recipe. This argument is set automatically at `mold()` time.
`extra_role_ptypes`	A named list. The names are the unique non-standard recipe roles (i.e. everything except `"predictors"` and `"outcomes"`). The values are prototypes of the original columns with that role. These are used for validation in `forge()`.