Want to know exactly what is going on, take a look at the Use it since this overview is not intended to be complete. You find a documentation of all java classes within WEKA. Outputs the complete class probability distribution. Predictions for test instances within a cross-validation. Program which utilizes various WEKA classes in order to give someįunctionality which is not yet integrated in WEKA - namely to output We will restrict ourselves toĬlassifiers and shortly note representatives for all main approachesĪfterwards, practical examples are given. Then we will focus on the machine learning algorithms themselves. Preprocessing, transformation, feature generation and so on. Then, we will describe the weka.filters package, which is used to transform input data, e.g. We will begin by describing basic concepts and Weka.jar? You can explicitly set CLASSPATH via the -cp command line option Get errors that classes are not found, check your CLASSPATH: does it include Get Out of Memory errors, increase the maximum heap sizeįor your java engine, usually via -Xmx1024M or -mx1024m for 1GB. Is not available via the GUI - and uses far less memory. Interface is recommended, because it offers some functionality which Interface is quite sufficient, for in-depth usage the command line While for initial experiments the included graphical user Removing the third class hierarchy level and renaming some classes). With versions 3-4-4 and above only (until the next reorganization, that is -)īasic concepts and issues can more easily be transferred to earlier versions,īut the specific examples may need to be slightly adapted (mostly Reorganization in class hierarchies for WEKA, all examples may only work This document serves as a practical introduction to the command line On using the command line interface to WEKA, although it is essential Offers documentation for the Knowledge Explorer and the Experimenter, and also some Tips & Tricks. However, WEKA is also quite complex to handle - amply demonstrated byĬoncerning the graphical user interface, the WEKA development group Regression, Association RulesĪnd clustering algorithms have also been implemented. Quite a few older ones - have been implemented within a clean, In the classification area, where all current ML approaches - and Toolbench for machine learning and data mining. Seewald - WEKA Command-Line Primer Introduction

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