Hi everyone!
New versions of Weka are available for download from the Weka homepage:
* Weka 3.6.13 - stable book 3rd edition version. It is available as ZIP, with Win32 installer, Win32 installer incl. JRE 1.7.0_80, Win64 installer, Win64 installer incl. 64 bit JRE 1.7.0_80 and Mac OS X application (both Oracle and Apple JVM versions).
* Weka 3.7.13 - development version. It is available as ZIP, with Win32 installer, Win32 installer incl. JRE 1.7.0_80, Win64 installer, Win64 installer incl. 64 bit JRE 1.7.0_80 and Mac OS X application (both Oracle and Apple JVM versions).
Both versions contain a significant number of bug fixes, it is recommended to upgrade to the new versions. Stable Weka 3.6 receives bug fixes only. The development version receives bug fixes and new features.
Weka homepage:
http://www.cs.waikato.ac.nz/~ml/weka/
Pentaho data mining community documentation:
http://wiki.pentaho.com/display/Pent...+Documentation
Packages for Weka>=3.7.2 can be browsed online at:
http://weka.sourceforge.net/packageMetaData/
Note: It might take a while before Sourceforge.net has propagated all the files to its mirrors.
What's new in 3.7.13?
Some highlights
---------------
In core weka:
* Numerically stable implementation of variance calculation in core Weka classes - thanks to Benjamin Weber
* Unified expression parsing framework (with compiled expressions) is now employed by filters and tools that use mathematical/logical expressions - thanks to Benjamin Weber
* Developers can now specify GUI and command-line options for their Weka schemes via a new unified annotation-based mechanism
* ClassConditionalProbabilities filter - replaces the value of a nominal attribute in a given instance with its probability given each of the possible class values
* GUI package manager's available list now shows both packages that are not currently installed, and those installed packages for which there is a more recent version available that is compatible with the base version of Weka being used
* ReplaceWithMissingValue filter - allows values to be randomly (with a user-specified probability) replaced with missing values. Useful for experimenting with methods for imputing missing values
* WrapperSubsetEval can now use plugin evaluation metrics
In packages:
* alternatingModelTrees package - alternating trees for regression
* timeSeriesFilters package, contributed by Benjamin Weber
* distributedWekaSpark package - wrapper for distributed Weka on Spark
* wekaPython package - execution of CPython scripts and wrapper classifier/clusterer for Scikit Learn schemes
* MLRClassifier in RPlugin now provides access to almost all classification and regression learners in MLR 2.4
As usual, for a complete list of changes refer to the changelogs.
Cheers,
The Weka Team