Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine...

Department of Computer Science, University of Waikato, New Zealand

Eibe Frank

WEKA: A Machine Learning Toolkit

The Explorer• Classification and

Regression• Clustering• Association Rules• Attribute Selection• Data Visualization

The Experimenter The Knowledge

Flow GUI Conclusions

Machine Learning with WEKA

based on notes by

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WEKA: the bird

Copyright: Martin Kramer (mkramer@wxs.nl)

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WEKA: the software Machine learning/data mining software written in

Java (distributed under the GNU Public License) Used for research, education, and applications Complements “Data Mining” by Witten & Frank Main features:

Comprehensive set of data pre-processing tools, learning algorithms and evaluation methods

Graphical user interfaces (incl. data visualization) Environment for comparing learning algorithms

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WEKA: versions There are several versions of WEKA:

WEKA 3.0: “book version” compatible with description in data mining book 1st edition

WEKA 3.2: “GUI version” adds graphical user interfaces (earlier version is command-line only)

WEKA 3.4 ++ on SoC linux and ISS windows This talk is based on snapshots of WEKA 3.3 … with some extra up-to-date snapshots Only changes are “layout” and some extras

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@relation heart-disease-simplified

@attribute age numeric@attribute sex { female, male}@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}@attribute cholesterol numeric@attribute exercise_induced_angina { no, yes}@attribute class { present, not_present}

@data63,male,typ_angina,233,no,not_present67,male,asympt,286,yes,present67,male,asympt,229,yes,present38,female,non_anginal,?,no,not_present...

WEKA only deals with “flat” files

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@relation heart-disease-simplified

@attribute age numeric@attribute sex { female, male}@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}@attribute cholesterol numeric@attribute exercise_induced_angina { no, yes}@attribute class { present, not_present}

@data63,male,typ_angina,233,no,not_present67,male,asympt,286,yes,present67,male,asympt,229,yes,present38,female,non_anginal,?,no,not_present...

WEKA only deals with “flat” files

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Explorer: pre-processing the data Data can be imported from a file in various

formats: ARFF, CSV, C4.5, binary Data can also be read from a URL or from an SQL

database (using JDBC) Pre-processing tools in WEKA are called “filters” BUT it may be easier to reformat to ARFF yourself

(write a program in python / java … or just use WordPad to type in the text – but make sure format is right!), this helps with data understanding

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Explorer: building “classifiers” Classifiers in WEKA are models for predicting

nominal or numeric quantities Implemented learning schemes include:

Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, …

You explore by trying different classifiers, see which works best for you…

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04/10/23 University of Waikato 44QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture.

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04/10/23 University of Waikato 46QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture.

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WEKA from ISS PC

2009

@relation ukus

@attribute center numeric@attribute centre numeric@attribute centerpercent numeric@attribute color numeric@attribute colour numeric@attribute colorpercent numeric@attribute english {UK,US}

@data1,32,3, 0,20,0, UK0,25,0, 0,12,0, UK9,27,25, 0,84,0, UK0,19,0, 0,24,0, UK0,16,0, 0,14,0, UK0,16,0, 0,12,0, UK0,21,0, 0,38,0, UK0,25,0, 0,34,0, UK2,26,7, 2,3,40, UK2,32,5, 1,59,2, UK31,0,100, 55,0,100, US61,0,100, 26,0,100, US24,0,100, 11,0,100, US12,1,92, 21,4,84, US8,0,100, 4,2,67, US10,0,100, 8,0,100, US19,0,100, 22,0,100, US14,0,100, 7,0,100, US14,0,100, 6,0,100, US8,5,62, 24,0,100, US

@relation test

@attribute center numeric@attribute centre numeric@attribute centerpercent numeric@attribute color numeric@attribute colour numeric@attribute colorpercent numeric@attribute english {UK,US}

@data10,5,33, 0,20,0, UK

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WEKA has more… Clustering data into groups Finding associations between attributes Visualisation - online analytical processing Experimenter to run and compare different MLs Knowledge Flow GUI 3rd-party add-ons: sourceforge.net http://www.cs.waikato.ac.nz/ml/weka