createRecipe: Creates recipes to preprocess data set

In amanda-park/easytidymodels: Make tidymodels even easier to run!

Description

Creates and returns a recipe object.

Usage

createRecipe(
  data = data,
  responseVar = "response",
  corrValue = 0.9,
  otherValue = 0.01
)

Arguments

data	The training data set
responseVar	the variable that is the response for analysis.
corrValue	The value to remove variables that are highly correlated from dataset. The step will try to remove the minimum number of columns so that all the resulting absolute correlations are less than this value. Default value is .9.
otherValue	The minimum frequency of a level in a factor variable needed to avoid converting its outcome to "other". Default is .01.

Details

The following data transformations are automatically applied to the data:

• Normalizes numeric variables

• Puts infrequent levels of categorical variables into "other" category

• Puts NA values into "unknown" category

• Removes variables with near-zero variance

• Removes highly correlated variables

• One-hot encodes your categorical variables

If a different recipe is needed, I recommend calling the recipes library and building one appropriate for your dataset (this function is hard to automate given the variety of data transformations that can happen for a specific data set).

Value

A recipes::recipe object that has been prepped.

Examples

library(easytidymodels)
library(dplyr)
utils::data(penguins, package = "modeldata")
resp <- "sex"
split <- trainTestSplit(penguins, stratifyOnResponse = TRUE, responseVar = resp)
formula <- stats::as.formula(paste(resp, ".", sep="~"))
rec <- createRecipe(split$train, responseVar = resp)
rec

amanda-park/easytidymodels documentation built on Dec. 13, 2021, 11:28 a.m.