Next | Query returned 8 messages, browsing 1 to 10 | previous

History of commit frequency

CVS Commit History:


   2024-01-28 09:21:07 by Thomas Klausner | Files touched by this commit (1)
Log message:
py-xgboost: insists on gcc 8.1+
   2024-01-24 23:45:54 by Adam Ciarcinski | Files touched by this commit (1)
Log message:
py-xgboost: remove unused REPLACE_; spotted by @wiz
   2024-01-24 23:43:20 by Adam Ciarcinski | Files touched by this commit (2)
Log message:
py-xgboost: fix build
   2024-01-19 15:36:17 by Adam Ciarcinski | Files touched by this commit (8) | Package updated
Log message:
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
   2023-08-02 01:20:57 by Thomas Klausner | Files touched by this commit (158)
Log message:
*: remove more references to Python 3.7
   2023-07-01 10:37:47 by Thomas Klausner | Files touched by this commit (105) | Package updated
Log message:
*: restrict py-numpy users to 3.9+ in preparation for update
   2023-06-19 10:03:48 by Adam Ciarcinski | Files touched by this commit (2) | Package updated
Log message:
py-xgboost: updated to 1.7.6

1.7.6 Patch Release

Bug Fixes

Fix distributed training with mixed dense and sparse partitions.
Fix monotone constraints on CPU with large trees.
[spark] Make the spark model have the same UID as its estimator
Optimize prediction with QuantileDMatrix.

Document

Improve doxygen
Update the cuDF pip index URL.

Maintenance

Fix tests with pandas 2.0.
   2023-06-13 19:36:58 by Adam Ciarcinski | Files touched by this commit (7)
Log message:
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.

Next | Query returned 8 messages, browsing 1 to 10 | previous