Output row to tree assignment for the model and provided training data.

Output is frame of size nrow = nrow(original_training_data) and ncol = number_of_trees_in_model+1 in format: row_id tree_1 tree_2 tree_3 0 0 1 1 1 1 1 1 2 1 0 0 3 1 1 0 4 0 1 1 5 1 1 1 6 1 0 0 7 0 1 0 8 0 1 1 9 1 0 0

row_to_tree_assignment.H2OModel(object, original_training_data, ...)

h2o.row_to_tree_assignment(object, original_training_data, ...)

Arguments

object

a fitted H2OModel object

original_training_data

An H2OFrame object that was used for model training. Currently there is no validation of the input.

...

additional arguments to pass on.

Value

Returns an H2OFrame contain row to tree assignment for each tree and row.

Details

Where 1 in the tree_{number} cols means row is used in the tree and 0 means that row is not used. The structure of the output depends on sample_rate or sample_size parameter setup.

Note: Multinomial classification generate tree for each category, each tree use the same sample of the data.

Examples

if (FALSE) { # \dontrun{
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.uploadFile(path = prostate_path)
prostate_gbm <- h2o.gbm(4:9, "AGE", prostate, sample_rate = 0.6)
# Get row to tree assignment
h2o.row_to_tree_assignment(prostate_gbm, prostate)
} # }