b4ec1682c6
* Group together categorical related parameters. * Update documents about multioutput and categorical.
39 lines
1.7 KiB
ReStructuredText
39 lines
1.7 KiB
ReStructuredText
################
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Multiple Outputs
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################
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.. versionadded:: 1.6
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Starting from version 1.6, XGBoost has experimental support for multi-output regression
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and multi-label classification with Python package. Multi-label classification usually
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refers to targets that have multiple non-exclusive class labels. For instance, a movie
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can be simultaneously classified as both sci-fi and comedy. For detailed explanation of
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terminologies related to different multi-output models please refer to the `scikit-learn
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user guide <https://scikit-learn.org/stable/modules/multiclass.HTML>`_.
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Internally, XGBoost builds one model for each target similar to sklearn meta estimators,
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with the added benefit of reusing data and other integrated features like SHAP. For a
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worked example of regression, see
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:ref:`sphx_glr_python_examples_multioutput_regression.py`. For multi-label classification,
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the binary relevance strategy is used. Input ``y`` should be of shape ``(n_samples,
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n_classes)`` with each column having a value of 0 or 1 to specify whether the sample is
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labeled as positive for respective class. Given a sample with 3 output classes and 2
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labels, the corresponding `y` should be encoded as ``[1, 0, 1]`` with the second class
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labeled as negative and the rest labeled as positive. At the moment XGBoost supports only
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dense matrix for labels.
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.. code-block:: python
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from sklearn.datasets import make_multilabel_classification
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import numpy as np
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X, y = make_multilabel_classification(
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n_samples=32, n_classes=5, n_labels=3, random_state=0
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)
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clf = xgb.XGBClassifier(tree_method="hist")
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clf.fit(X, y)
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np.testing.assert_allclose(clf.predict(X), y)
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The feature is still under development with limited support from objectives and metrics.
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