R/models.R
row_to_tree_assignment.H2OModel.Rd
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, ...)
object | a fitted H2OModel object |
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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. |
Returns an H2OFrame contain row to tree assignment for each tree and row.
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.
# NOT RUN { 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) # }