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March 25, 2004Columbia University1 Machine Learning with Weka Lokesh S. Shrestha.

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1 March 25, 2004Columbia University1 Machine Learning with Weka Lokesh S. Shrestha

2 March 25,2004 Columbia University 2 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

3 March 25,2004 Columbia University 3 @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} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present... WEKA only deals with “flat” files

4 March 25,2004 Columbia University 4 @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} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present... WEKA only deals with “flat” files

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6 March 25,2004 Columbia University 6 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” WEKA contains filters for:  Discretization, normalization, resampling, attribute selection, transforming and combining attributes, …

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12 March 25,2004 Columbia University 12 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, … “Meta”-classifiers include:  Bagging, boosting, stacking, error-correcting output codes, locally weighted learning, …

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37 March 25,2004 Columbia University 37 Explorer: clustering data WEKA contains “clusterers” for finding groups of similar instances in a dataset Implemented schemes are:  k-Means, EM, Cobweb, X-means, FarthestFirst Clusters can be visualized and compared to “true” clusters (if given) Evaluation based on loglikelihood if clustering scheme produces a probability distribution

38 March 25,2004 Columbia University 38 Explorer: finding associations WEKA contains an implementation of the Apriori algorithm for learning association rules  Works only with discrete data Can identify statistical dependencies between groups of attributes:  milk, butter  bread, eggs (with confidence 0.9 and support 2000) Apriori can compute all rules that have a given minimum support and exceed a given confidence

39 March 25,2004 Columbia University 39 Explorer: attribute selection Panel that can be used to investigate which (subsets of) attributes are the most predictive ones Attribute selection methods contain two parts:  A search method: best-first, forward selection, random, exhaustive, genetic algorithm, ranking  An evaluation method: correlation-based, wrapper, information gain, chi-squared, … Very flexible: WEKA allows (almost) arbitrary combinations of these two

40 March 25,2004 Columbia University 40 Explorer: data visualization Visualization very useful in practice: e.g. helps to determine difficulty of the learning problem WEKA can visualize single attributes (1-d) and pairs of attributes (2-d)  To do: rotating 3-d visualizations (Xgobi-style) Color-coded class values “Jitter” option to deal with nominal attributes (and to detect “hidden” data points) “Zoom-in” function

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43 March 25,2004 Columbia University 43 Performing experiments Experimenter makes it easy to compare the performance of different learning schemes For classification and regression problems Results can be written into file or database Evaluation options: cross-validation, learning curve, hold- out Can also iterate over different parameter settings Significance-testing built in!

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55 March 25,2004 Columbia University 55 Conclusion: try it yourself! WEKA is available at http://www.cs.waikato.ac.nz/ml/weka  Also has a list of projects based on WEKA  WEKA contributors: Abdelaziz Mahoui, Alexander K. Seewald, Ashraf M. Kibriya, Bernhard Pfahringer, Brent Martin, Peter Flach, Eibe Frank,Gabi Schmidberger,Ian H. Witten, J. Lindgren, Janice Boughton, Jason Wells, Len Trigg, Lucio de Souza Coelho, Malcolm Ware, Mark Hall,Remco Bouckaert, Richard Kirkby, Shane Butler, Shane Legg, Stuart Inglis, Sylvain Roy, Tony Voyle, Xin Xu, Yong Wang, Zhihai Wang

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