New in version 1.6.
Starting from version 1.6, XGBoost has experimental support for multi-output regression and multi-label classification with Python package. Multi-label classification usually refers to targets that have multiple non-exclusive class labels. For instance, a movie can be simultaneously classified as both sci-fi and comedy. For detailed explanation of terminologies related to different multi-output models please refer to the scikit-learn user guide.
Internally, XGBoost builds one model for each target similar to sklearn meta estimators,
with the added benefit of reusing data and other integrated features like SHAP. For a
worked example of regression, see
A demo for multi-output regression. For multi-label classification,
the binary relevance strategy is used. Input
y should be of shape
n_classes) with each column having a value of 0 or 1 to specify whether the sample is
labeled as positive for respective class. Given a sample with 3 output classes and 2
labels, the corresponding y should be encoded as
[1, 0, 1] with the second class
labeled as negative and the rest labeled as positive. At the moment XGBoost supports only
dense matrix for labels.
from sklearn.datasets import make_multilabel_classification import numpy as np X, y = make_multilabel_classification( n_samples=32, n_classes=5, n_labels=3, random_state=0 ) clf = xgb.XGBClassifier(tree_method="hist") clf.fit(X, y) np.testing.assert_allclose(clf.predict(X), y)
The feature is still under development with limited support from objectives and metrics.