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From Ed Espino <>
Subject [Announce] Apache MADlib v1.13 released
Date Fri, 29 Dec 2017 06:01:14 GMT
The Apache MADlib team is pleased to announce the immediate
availability of the 1.13 release.

The main goals of this release are:

New features:

* New module: Graph - HITS (MADLIB-1124, MADLIB-1151)
* k-NN:
  - Added additional distance metrics (MADLIB-1059)
  - Added list of neighbors in output table (MADLIB-1129)
* MLP: Added grouping support (MADLIB-1149)
* Cross Validation: Improved the stats reporting in output table
* Correlation: Improved quality of results by ignoring only a NULL
  value and not the whole row containing the NULL (MADLIB-1166)

Bug fixes:

  - Fixed issue with Decision Trees (DT) trained in older versions not
    being usable in predict of v1.12 (MADLIB-1161)
  - Fixed invalid assert statement in DT (MADLIB-1164)
  - Improved feature array handling in DT (MADLIB-1173)
  - Fixed install-check failures on non-default schema installation
    (MADLIB-1177, 1184)

  - Updated PyXB from 1.2.4 to 1.2.6. (MADLIB-1103)
    This change eliminates the need to remove part of PyXB codebase as
    a GPL-workaround.
  - Updated the naming for gppkg (MADLIB-1183)

All release changes can be found here:

You can download the source release and convenience binary packages
from Apache MADlib's download page here:

Alternatively, you can download through an ASF mirror near you:


Apache MADlib is an open-source library for scalable in-database
analytics. It provides data-parallel implementations of mathematical,
statistical and machine learning methods for structured and
unstructured data.

The MADlib mission: to foster widespread development of scalable
analytic skills, by harnessing efforts from commercial practice,
academic research, and open-source development.

We welcome your help and feedback. For more information on how to
report problems, and to get involved, visit the project website at


Thank you, everyone who contributed to the MADlib 1.13 release. We
look forward to continued community participation for the next

Your Apache MADlib team

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