fast_classification {tidyAML}

Generate Model Specification calls to parsnip

Description

Creates a list/tibble of parsnip model specifications.

Usage

fast_classification(
  .data,
  .rec_obj,
  .parsnip_fns = "all",
  .parsnip_eng = "all",
  .split_type = "initial_split",
  .split_args = NULL,
  .drop_na = TRUE
)

Arguments

.data

The data being passed to the function for the classification problem

.rec_obj

The recipe object being passed.

.parsnip_fns

The default is 'all' which will create all possible classification model specifications supported.

.parsnip_eng

the default is 'all' which will create all possible classification model specifications supported.

.split_type

The default is 'initial_split', you can pass any type of split supported by rsample

.split_args

The default is NULL, when NULL then the default parameters of the split type will be executed for the rsample split type.

.drop_na

The default is TRUE, which will drop all NA's from the data.

Details

With this function you can generate a tibble output of any classification model specification and it's fitted workflow object. Per recipes documentation explicitly with step_string2factor() it is encouraged to mutate your predictor into a factor before you create your recipe.

Value

A list or a tibble.

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Model_Generator: create_model_spec(), fast_regression()

Examples

library(recipes)
library(dplyr)
library(tidyr)

df <- Titanic |>
 as_tibble() |>
 uncount(n) |>
 mutate(across(everything(), as.factor))

rec_obj <- recipe(Survived ~ ., data = df)

fct_tbl <- fast_classification(
  .data = df,
  .rec_obj = rec_obj,
  .parsnip_eng = c("glm","earth")
  )

fct_tbl