Slicing Models

Slice tree model

When booster is set to gbtree or dart, XGBoost builds a tree model, which is a list of trees and can be sliced into multiple sub-models.

PythonR

import xgboost as xgb
from sklearn.datasets import make_classification
num_classes = 3
X, y = make_classification(n_samples=1000, n_informative=5,
                           n_classes=num_classes)
dtrain = xgb.DMatrix(data=X, label=y)
num_parallel_tree = 4
num_boost_round = 16
# total number of built trees is num_parallel_tree * num_classes * num_boost_round

# We build a boosted random forest for classification here.
booster = xgb.train({
    'num_parallel_tree': 4, 'subsample': 0.5, 'num_class': 3},
                    num_boost_round=num_boost_round, dtrain=dtrain)

# This is the sliced model, containing [3, 7) forests
# step is also supported with some limitations like negative step is invalid.
sliced: xgb.Booster = booster[3:7]

# Access individual tree layer
trees = [_ for _ in booster]
assert len(trees) == num_boost_round

The sliced model is a copy of selected trees, that means the model itself is immutable during slicing. This feature is the basis of save_best option in early stopping callback. See Demo for prediction using individual trees and model slices for a worked example on how to combine prediction with sliced trees.

Note

The returned model slice doesn’t contain attributes like best_iteration and best_score.