You can then use the excellent graph visualization package from AT&T called Graphviz ( ) to generate very attractive graphs/trees in many different formats (gif, jpeg, postscript, eps, png etc.). This can be done from the command line using the -g option. This plugin will be made into a package that can be installed automatically via the package manager.Ī second option is to use J48's ability to export its tree in the dot graph language. The next release of the development version of Weka (3.7.2) will include a new package management system.
After reading this post you will know: About 5 top regression algorithms supported by Weka.
#WEKA JAR USAGE HOW TO#
In this post you will discover how to use top regression machine learning algorithms in Weka. There are full instructions and a screenshot at: The large number of machine learning algorithms supported by Weka is one of the biggest benefits of using the platform. You'll need to compile the plugin though. One is to use a plugin for Weka that uses the Prefuse visualization toolkit ( ). There are some layout options available by right-clicking anywhere on the canvas, but I suspect that it won't help a great deal in your case. We need to add this jar as a classpath to our program. Now we can find all the information about the classes and methods in the Weka Java API documentation. The JAR file contains all the class files required i.e. After downloading the archive and extracting it you’ll find the weka.jar file.
Yes, I see :-) Weka's built-in tree visualizer isn't very good for visualizing large tree structures. To use the weka API you need to install weka according to your operating system. :moa.jar:weka.jar -javaagent:sizeofag.jar moa.DoTask EvaluateModel -m file:amodel.moa -s (ArffFileStream -f atest.