target_encoder: target_encoder

In UBC-MDS/encodeR: A collection of categorical encoders in R

Description

This function encodes categorical variables with average target values for each category.

Usage

target_encoder(
  X_train,
  X_test = NULL,
  y,
  cat_columns,
  prior = 0.5,
  objective = "regression"
)

Arguments

X_train	A 'tibble' or 'data.frame' representing the training data set containing some categorical features/columns.
X_test	A 'tibble' or 'data.frame' representing the test set, containing some set of categorical features/columns.
y	A numeric vector or character vector representing the target variable. If the objective is "binary", then the vector should only contain two unique values.
cat_columns	A character vector containing the names of the categorical columns in the tibble that should be encoded.
prior	A number in [0, inf] that acts as pseudo counts when calculating the encodings. Useful for preventing encodings of 0 for when the training set does not have particular categories observed in the test set. A larger value gives less weight to what is observed in the training set. A value of 0 incorporates no prior information. The default value is 0.5.
objective	A string, either "regression" or "binary" specifying the problem. Default is regression.

Value

A list containing with processed training and test sets, in which the named categorical columns are replaced with their encodings.

Examples

target_encoder(
X_train = mtcars,
y = mtcars$mpg,
cat_columns = c("gear", "carb"),
prior = 0.5,
objective = "regression")

UBC-MDS/encodeR documentation built on March 31, 2020, 12:53 a.m.