After a long period of development, XGBoost v1.6.0 is packed with many new features and
improvements. We summarize them in the following sections starting with an introduction to
some major new features, then moving on to language binding specific changes including new
features and notable bug fixes for that binding.
Development of categorical data support
This version of XGBoost features new improvements and full coverage of experimental
categorical data support in Python and C package with tree model. Both hist, approx
and gpu_hist now support training with categorical data. Also, partition-based
categorical split is introduced in this release. This split type is first available in
LightGBM in the context of gradient boosting. The previous XGBoost release supported one-hot split where the splitting criteria is of form x \in {c}, i.e. the categorical feature x is tested against a single candidate. The new release allows for more expressive conditions: x \in S where the categorical feature x is tested against multiple candidates. Moreover, it is now possible to use any tree algorithms (hist, approx, gpu_hist) when creating categorical splits. For more
information, please see our tutorial on categorical
data, along with
examples linked on that page. (#7380, #7708, #7695, #7330, #7307, #7322, #7705,
#7652, #7592, #7666, #7576, #7569, #7529, #7575, #7393, #7465, #7385, #7371, #7745, #7810)
In the future, we will continue to improve categorical data support with new features and
optimizations. Also, we are looking forward to bringing the feature beyond Python binding,
contributions and feedback are welcomed! Lastly, as a result of experimental status, the
behavior might be subject to change, especially the default value of related
hyper-parameters.
Experimental support for multi-output model
XGBoost 1.6 features initial support for the multi-output model, which includes
multi-output regression and multi-label classification. Along with this, the XGBoost
classifier has proper support for base margin without to need for the user to flatten the
input. In this initial support, XGBoost builds one model for each target similar to the
sklearn meta estimator, for more details, please see our quick
introduction.
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Updates the requirements on xgboost to permit the latest version.
Changelog
Sourced from xgboost's changelog.
... (truncated)
Commits
b993424
Fix typo. (#8192)1fbb452
Fix release script. (#8187)0fd6391
[backport] Fix loading DMatrix binary in distributed env. (#8149) (#8185)922d213
[CI] Fix R build on Jenkins. (#8154) (#8180)7036d4f
Disable modin test on 1.6.0 branch. (#8176)2d54f7d
Make 1.6.2 patch release. (#8175)51c3301
[backport] Fix LTR with weighted Quantile DMatrix. (#7975) (#8170)e82162d
[backport] Fix Python package source install. (#8036) (#8171)b18c984
[dask] Deterministic rank assignment. (#8018) (#8165)2e6444b
[backport] Limitmax_depth
to 30 for GPU. (#8098) (#8169)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
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