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#####
Model
#####
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.
.. code-block:: python
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 :ref:`sphx_glr_python_examples_individual_trees.py` for a worked example on
how to combine prediction with sliced trees.
.. note::
The returned model slice doesn't contain attributes like :py:class:`~xgboost.Booster.best_iteration` and :py:class:`~xgboost.Booster.best_score`.
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