Applies a target encoding map to an H2OFrame object. 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_apply(data, x, y, target_encode_map, holdout_type,
fold_column = NULL, blended_avg = TRUE, noise_level = NULL,
seed = -1)

## Arguments

data An H2OFrame object with which to apply the target encoding map. A list containing the names or indices of the variables to encode. A target encoding column 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 the resulting frame will have a target encoding column for A and a target encoding column for B & C (in this case, we group by two columns). The name or column index of the response variable in the data. The response variable can be either numeric or binary. A list of H2OFrame objects that is the results of the h2o.target_encode_create function. The holdout type used. Must be one of: "LeaveOneOut", "KFold", "None". (Optional) The name or column index of the fold column in the data. Defaults to NULL (no fold_column). Only required if holdout_type = "KFold". Logical. (Optional) Whether to perform blended average. (Optional) The amount of random noise added to the target encoding. This helps prevent overfitting. Defaults to 0.01 * range of y. (Optional) A random seed used to generate draws from the uniform distribution for random noise. Defaults to -1.

## Value

Returns an H2OFrame object containing the target encoding per record.

## See also

h2o.target_encode_create for creating the target encoding map

## Examples

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

# Get Target Encoding Frame on bank-additional-full data with numeric y
data <- h2o.importFile(
path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/demos/bank-additional-full.csv")
splits <- h2o.splitFrame(data, seed = 1234)
train <- splits[[1]]
test <- splits[[2]]
mapping <- h2o.target_encode_create(data = train, x = list(c("job"), c("job", "marital")),
y = "age")

# Apply mapping to the training dataset
train_encode <- h2o.target_encode_apply(data = train, x = list(c("job"), c("job", "marital")),
y = "age", mapping, holdout_type = "LeaveOneOut")
# Apply mapping to a test dataset
test_encode <- h2o.target_encode_apply(data = test, x = list(c("job"), c("job", "marital")),
y = "age", target_encode_map = mapping,
holdout_type = "None")

# }