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

Usage

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)
} # }