The H2OTree class.

This class represents a model of a Tree built by one of H2O's algorithms (GBM, Random Forest).

# S4 method for H2OTree
show(object)

Arguments

object	an H2OTree object.

Slots

root_node

A H2ONode representing the beginning of the tree behind the model. Allows further tree traversal.

left_children

An integer vector with left child nodes of tree's nodes

right_children

An integer vector with right child nodes of tree's nodes

node_ids

An integer representing identification number of a node. Node IDs are generated by H2O.

descriptions

A character vector with descriptions for each node to be found in the tree. Contains split threshold if the split is based on numerical column. For cactegorical splits, it contains list of categorical levels for transition from the parent node.

model_id

A character with the name of the model this tree is related to.

tree_number

An integer representing the order in which the tree has been built in the model.

tree_class

A character representing name of tree's class. Number of tree classes equals to the number of levels in categorical response column. As there is exactly one class per categorical level, name of tree's class equals to the corresponding categorical level of response column. In case of regression and binomial, the name of the categorical level is ignored can be omitted, as there is exactly one tree built in both cases.

thresholds

A numeric split thresholds. Split thresholds are not only related to numerical splits, but might be present in case of categorical split as well.

features

A character with names of the feature/column used for the split.

levels

A character representing categorical levels on split from parent's node belonging into this node. NULL for root node or non-categorical splits.

nas

A character representing if NA values go to the left node or right node. May be NA if node is a leaf.

predictions

A numeric representing predictions for each node in the graph.

tree_decision_path

A character, plain language rules representation of a trained decision tree

decision_paths

A character representing plain language rules that were used in a particular prediction.

left_cat_split

A character list of categorical levels leading to the left child node. Only present when split is categorical, otherwise none.

right_cat_split

A character list of categorical levels leading to the right child node. Only present when split is categorical, otherwise none.