head	1.7;
access;
symbols
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locks; strict;
comment	@# @;


1.7
date	2026.10.23.12.26.56;	author adam;	state Exp;
branches;
next	1.6;
commitid	upQRD2EjfAqsbB0H;

1.6
date	2026.06.24.05.28.22;	author adam;	state Exp;
branches;
next	1.5;
commitid	IyOzm8ilb0z0L0LG;

1.5
date	2026.02.16.11.13.35;	author adam;	state Exp;
branches;
next	1.4;
commitid	p8XfBKIoau2AMAuG;

1.4
date	2025.12.26.16.36.54;	author adam;	state Exp;
branches;
next	1.3;
commitid	PL3reOimpTn5fWnG;

1.3
date	2024.08.04.13.05.59;	author adam;	state Exp;
branches;
next	1.2;
commitid	84xFZ42Rlex2rvkF;

1.2
date	2024.01.19.14.36.17;	author adam;	state Exp;
branches;
next	1.1;
commitid	NievF4jfuWiEi4VE;

1.1
date	2023.06.13.17.36.58;	author adam;	state Exp;
branches;
next	;
commitid	GONqMQ9maOe1nOsE;


desc
@@


1.7
log
@py-xgboost: updated to 3.4.1

3.4.1

[jvm] Fix batch predict for SparseVector features
Fix model slicing with category container.
@
text
@@@comment $NetBSD: PLIST,v 1.6 2026/06/24 05:28:22 adam Exp $
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@


1.6
log
@py-xgboost: updated to 3.3.0

XGBoost 3.3 adds expectile regression, enables categorical feature support by
default, expands SHAP support for vector-leaf models, and includes
optimizations for histogram building, quantile sketching, and distributed GPU
training.
@
text
@d1 1
a1 1
@@comment $NetBSD: PLIST,v 1.5 2026/02/16 11:13:35 adam Exp $
d5 1
@


1.5
log
@py-xgboost: updated to 3.2.0

3.2.0

We are excited to announce the XGBoost 3.2 release. This release features
significant progress on multi-target tree support with vector leaf, enhanced
GPU external memory training, various optimizations, and the removal of the
deprecated CLI.
@
text
@d1 1
a1 1
@@comment $NetBSD: PLIST,v 1.4 2025/12/26 16:36:54 adam Exp $
d9 3
d48 3
@


1.4
log
@py-xgboost: updated to 3.1.2

3.1.2
Fix ordering of Python callbacks.
Fix loading nccl 2.28.
Infer the enable_categorical during model load.
@
text
@d1 1
a1 1
@@comment $NetBSD: PLIST,v 1.3 2024/08/04 13:05:59 adam Exp $
d49 3
d110 3
d119 3
@


1.3
log
@py-xgboost: updated to 2.1.1

The 2.1.1 patch release make the following bug fixes:

[Dask] Disable broadcast in the scatter call so that predict function won't hang
[Dask] Handle empty partitions correctly
Fix federated learning for the encrypted GRPC backend
Fix a race condition in column splitter
Gracefully handle cases where system files like /sys/fs/cgroup/cpu.max are not readable by the user
Fix build and C++ tests for FreeBSD
Clarify the requirement Pandas 1.2+
More robust endianness detection in R package build

In addition, it contains several enhancements:

Publish JVM packages targeting Linux ARM64
Publish a CPU-only wheel under name xgboost-cpu
Support building with CUDA Toolkit 12.5 and latest CCCL
@
text
@d1 1
a1 1
@@comment $NetBSD: PLIST,v 1.2 2024/01/19 14:36:17 adam Exp $
d9 3
d33 3
d71 3
d80 9
d104 3
d110 6
d119 12
d140 6
@


1.2
log
@py-xgboost: updated to 2.0.3

2.0.3

[backport][sklearn] Fix loading model attributes.
[backport][py] Use the first found native library.
[backport] [CI] Upload libxgboost4j.dylib (M1) to S3 bucket
[jvm-packages] Fix POM for xgboost-jvm metapackage
@
text
@d1 1
a1 1
@@comment $NetBSD: PLIST,v 1.1 2023/06/13 17:36:58 adam Exp $
d27 6
a32 3
${PYSITELIB}/xgboost/dask.py
${PYSITELIB}/xgboost/dask.pyc
${PYSITELIB}/xgboost/dask.pyo
a46 3
${PYSITELIB}/xgboost/rabit.py
${PYSITELIB}/xgboost/rabit.pyc
${PYSITELIB}/xgboost/rabit.pyo
d71 3
d83 3
@


1.1
log
@py-xgboost: added version 1.7.5

XGBoost is an optimized distributed gradient boosting library designed to be
highly efficient, flexible and portable. It implements machine learning
algorithms under the Gradient Boosting framework. XGBoost provides a parallel
tree boosting (also known as GBDT, GBM) that solve many data science problems
in a fast and accurate way. The same code runs on major distributed environment
(Kubernetes, Hadoop, SGE, Dask, Spark, PySpark) and can solve problems beyond
billions of examples.
@
text
@d1 4
a4 9
@@comment $NetBSD: PLIST,v 1.9 2023/05/08 08:51:03 adam Exp $
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