gpb.model.dt.tree: Parse a GPBoost model json dump

In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

Parse a GPBoost model json dump

Description

Parse a GPBoost model json dump into a data.table structure.

Usage

gpb.model.dt.tree(model, num_iteration = NULL)

Arguments

model	object of class gpb.Booster
num_iteration	number of iterations you want to predict with. NULL or <= 0 means use best iteration

Value

A data.table with detailed information about model trees' nodes and leafs.

The columns of the data.table are:

• tree_index: ID of a tree in a model (integer)

• split_index: ID of a node in a tree (integer)

• split_feature: for a node, it's a feature name (character); for a leaf, it simply labels it as "NA"

• node_parent: ID of the parent node for current node (integer)

• leaf_index: ID of a leaf in a tree (integer)

• leaf_parent: ID of the parent node for current leaf (integer)

• split_gain: Split gain of a node

• threshold: Splitting threshold value of a node

• decision_type: Decision type of a node

• default_left: Determine how to handle NA value, TRUE -> Left, FALSE -> Right

• internal_value: Node value

• internal_count: The number of observation collected by a node

• leaf_value: Leaf value

• leaf_count: The number of observation collected by a leaf

Examples

data(agaricus.train, package = "gpboost")
train <- agaricus.train
dtrain <- gpb.Dataset(train$data, label = train$label)

params <- list(
  objective = "binary"
  , learning_rate = 0.01
  , num_leaves = 63L
  , max_depth = -1L
  , min_data_in_leaf = 1L
  , min_sum_hessian_in_leaf = 1.0
)
model <- gpb.train(params, dtrain, 10L)

tree_dt <- gpb.model.dt.tree(model)

gpboost documentation built on Nov. 5, 2025, 7:34 p.m.