Create Target Encoding Map
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. A Target Encoding tutorial is available here: https://github.com/h2oai/h2o-tutorials/blob/master/best-practices/categorical-predictors/target_encoding.md.
h2o.target_encode_create(data, x, y, fold_column = NULL)
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'). |
Returns a list of H2OFrame objects containing the target encoding mapping for each column in 'x'.
h2o.target_encode_apply for applying the target encoding mapping to a frame.
## 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")
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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.