xgb.model.dt.tree
Parse a boosted tree model text dump
Parse a boosted tree model text dump into a data.table structure.
Usage
xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
n_first_tree = NULL)Arguments
- feature_names
character vector of feature names. If the model already contains feature names, this argument should be
NULL(default value)- model
object of class
xgb.Booster- text
charactervector previously generated by thexgb.dumpfunction (where parameterwith_stats = TRUEshould have been set).- n_first_tree
limit the parsing to the
nfirst trees. If set toNULL, all trees of the model are parsed.
Value
A data.table with detailed information about model trees' nodes.
The columns of the data.table are:
Tree: ID of a tree in a modelNode: ID of a node in a treeID: unique identifier of a node in a modelFeature: for a branch node, it's a feature id or name (when available); for a leaf note, it simply labels it as'Leaf'Split: location of the split for a branch node (split condition is always "less than")Yes: ID of the next node when the split condition is metNo: ID of the next node when the split condition is not metMissing: ID of the next node when branch value is missingQuality: either the split gain (change in loss) or the leaf valueCover: metric related to the number of observation either seen by a split or collected by a leaf during training.
Examples
# NOT RUN {
# Basic use:
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
(dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
# How to match feature names of splits that are following a current 'Yes' branch:
merge(dt, dt[, .(ID, Y.Feature=Feature)], by.x='Yes', by.y='ID', all.x=TRUE)[order(Tree,Node)]
# }