Create Target Encoding Map

Source: R/frame.R

Creates a target encoding map based on group-by columns (`x`) and a numeric or binary target column (`y`). Computing target encoding for high cardinality categorical columns can improve performance of supervised learning models.

h2o.target_encode_create(data, x, y, fold_column = NULL)

Arguments

data	An H2OFrame object with which to create the target encoding map.
x	A list containing the names or indices of the variables to encode. A target encoding map will be created for each element in the list. Items in the list can be multiple columns. For example, if `x = list(c("A"), c("B", "C"))`, then there will be one mapping frame for A and one mapping frame for B & C (in this case, we group by two columns).
y	The name or column index of the response variable in the data. The response variable can be either numeric or binary.
fold_column	(Optional) The name or column index of the fold column in the data. Defaults to NULL (no `fold_column`).

Value

Returns a list of H2OFrame objects containing the target encoding mapping for each column in `x`.

See also

h2o.target_encode_apply for applying the target encoding mapping to a frame.

Examples

# NOT RUN {
library(h2o)
h2o.init()

# Get Target Encoding Map on bank-additional-full data with numeric response
data <- h2o.importFile(
path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv",
destination_frame = "data")
mapping_age <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")),
y = "age")
head(mapping_age)

# Get Target Encoding Map on bank-additional-full data with binary response
mapping_y <- h2o.target_encode_create(data = data, x = list(c("job"), c("job", "marital")),
y = "y")
head(mapping_y)

# }