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

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



object of class lgb.Booster


number of iterations you want to predict with. NULL or <= 0 means use best iteration


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


data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.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 <- lgb.train(params, dtrain, 10L) tree_dt <- lgb.model.dt.tree(model)