xgb.model.dt.tree {xgboost} | R Documentation |
Parse a boosted tree model text dump into a data.table
structure.
xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL, n_first_tree = NULL)
feature_names |
character vector of feature names. If the model already
contains feature names, this argument should be |
model |
object of class |
text |
|
n_first_tree |
limit the parsing to the |
A data.table
with detailed information about model trees' nodes.
The columns of the data.table
are:
Tree
: ID of a tree in a model
Node
: ID of a node in a tree
ID
: unique identifier of a node in a model
Feature
: 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 met
No
: ID of the next node when the split condition is not met
Missing
: ID of the next node when branch value is missing
Quality
: either the split gain (change in loss) or the leaf value
Cover
: metric related to the number of observation either seen by a split
or collected by a leaf during training.
# 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)]