Weka is a collection of machine learning algorithms and data processing tools written in Java. It takes input in ARFF files and uses classifiers and filters to analyze the data. Weka has four modes: Explorer, Knowledge Flow, Experimenter, and a simple CLI. It has online documentation and APIs for developers as well as free downloads available on its website.

1. Introduction to Weka

2. IntroductionData mining isan experimental science Weka Workbench is a collection of state of the art machine learning algorithms and data processing toolsIt is written in java and available for Linux, Windows on Macintosh platform

3. What’s in Weka?Input is taken in the form of ARFF filesLearning methods are called classifiers Classifiers have tunable parameters which you can access through a property sheet or object editor Filters are used to pre process the data

4. How to use it?There are four modes through which one can use Weka:ExplorerKnowledge Flow allows you to design configurations for streamed data processing Experimenter allows an automated means to run classifiers and filters with different parameter settings on a corpus of datasets, collect performance statistics, and perform significance testsSimple CLI provides a command line mode to access Weka

5. What else is there?Weka has online documentation produced directly from the source code and describes the structure It gives list of available algorithms The application programming interface for Weka is also thereWeka can be downloaded for free from http://www.cs.waikato.ac.nz/ml/weka

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