lifecycle::badge("experimental")
Examples of preprocess functions that can be used in
cross_validate_fn() and
validate_fn().
They can either be used directly or be starting points.
The examples use recipes,
but you can also use caret::preProcess() or
similar functions.
In these examples, the preprocessing will only affect the numeric predictors.
You may prefer to hardcode a formula like "y ~ ." (where
y is your dependent variable) as that will allow you to set
`preprocess_one` to TRUE in cross_validate_fn()
and validate_fn() and save time.
preprocess_functions(name)A function with the following form:
function(train_data, test_data, formula, hyperparameters) {
# Preprocess train_data and test_data
# Return a list with the preprocessed datasets
# and optionally a data frame with preprocessing parameters
list(
"train" = train_data,
"test" = test_data,
"parameters" = tidy_parameters
)
}
Name of preprocessing function as it appears in the following list:
| Name | Description | "standardize" |
| Centers and scales the numeric predictors | "range" | Normalizes the numeric predictors to the 0-1 range |
| "scale" | Scales the numeric predictors to have a standard deviation of one | "center" |
| Centers the numeric predictors to have a mean of zero | "warn" | Identity function that throws a warning and a message |
Ludvig Renbo Olsen, r-pkgs@ludvigolsen.dk
Other example functions:
model_functions(),
predict_functions(),
update_hyperparameters()