xgb.model.dt.tree
From xgboost v0.6-3
by Tong He
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
character
vector previously generated by thexgb.dump
function (where parameterwith_stats = TRUE
should have been set).- n_first_tree
- limit the parsing to the
n
first trees. If set toNULL
, all trees of the model are parsed.
Value
-
A
-
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.
data.table
with detailed information about model trees' nodes.The columns of the data.table
are:Examples
# 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)]
Community examples
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