knitr::opts_chunk$set( message = FALSE, digits = 3, collapse = TRUE, comment = "#>", eval = requireNamespace("modeldata", quietly = TRUE) ) options(digits = 3)
When recipe steps are used, there are different approaches that can be used to select which variables or features should be used.
The three main characteristics of variables that can be queried:
The manual pages for
?has_role have details about the available selection methods.
To illustrate this, the credit data will be used:
library(recipes) library(modeldata) data("credit_data") str(credit_data) rec <- recipe(Status ~ Seniority + Time + Age + Records, data = credit_data) rec
Before any steps are used the information on the original variables is:
summary(rec, original = TRUE)
We can add a step to compute dummy variables on the non-numeric data after we impute any missing data:
dummied <- rec %>% step_dummy(all_nominal())
This will capture any variables that are either character strings or factors:
Records. However, since
Status is our outcome, we might want to keep it as a factor so we can subtract that variable out either by name or by role:
dummied <- rec %>% step_dummy(Records) # or dummied <- rec %>% step_dummy(all_nominal(), - Status) # or dummied <- rec %>% step_dummy(all_nominal_predictors())
Using the last definition:
dummied <- prep(dummied, training = credit_data) with_dummy <- bake(dummied, new_data = credit_data) with_dummy
Status is unaffected.
One important aspect about selecting variables in steps is that the variable names and types may change as steps are being executed. In the above example,
Records is a factor variable before the step is executed. Afterwards,
Records is gone and the binary variable
Records_yes is in its place. One reason to have general selection routines like
contains() is to be able to select variables that have not be created yet.
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