Slicing Models

.. code-tab:: py

    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

.. code-tab:: r R

    library(xgboost)
    data(agaricus.train, package = "xgboost")
    dm <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)

    model <- xgb.train(
      params = xgb.params(objective = "binary:logistic", max_depth = 4),
      data = dm,
      nrounds = 20
    )
    sliced <- model[seq(3, 7)]
    ##### xgb.Booster
    # of features: 126
    # of rounds:  5