WEKA: the bird. Machine Learning with WEKA. WEKA: A Machine Learning Toolkit The Explorer. Eibe Frank
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1 Machine Learning with WEKA Eibe Frank Department of Computer Science, University of Waikato, New Zealand WEKA: A Machine Learning Toolkit The Explorer Classification and Regression Clustering Association Rules Attribute Selection Data Visualization The Experimenter The Knowledge Flow GUI Conclusions WEKA: the bird Copyright: Martin Kramer (mkramer@wxs.nl) 2/4/2004 University of Waikato 2 Machine Learning for Data Mining 1
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 2/4/2004 University of Waikato 3 WEKA: versions There are several versions of WEKA: WEKA 3.0: book version compatible with description in data mining book WEKA 3.2: GUI version adds graphical user interfaces (book version is command-line only) WEKA 3.3: development version with lots of improvements This talk is based on the latest snapshot of WEKA 3.3 (soon to be WEKA 3.4) 2/4/2004 University of Waikato 4 Machine Learning for Data Mining 2
3 WEKA only deals with flat age sex { female, chest_pain_type { typ_angina, asympt, non_anginal, cholesterol exercise_induced_angina { no, class { present, 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... 2/4/2004 University of Waikato 5 WEKA only deals with flat age sex { female, chest_pain_type { typ_angina, asympt, non_anginal, cholesterol exercise_induced_angina { no, class { present, 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... 2/4/2004 University of Waikato 6 Machine Learning for Data Mining 3
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5 2/4/2004 University of Waikato 9 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, 2/4/2004 University of Waikato 10 Machine Learning for Data Mining 5
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16 2/4/2004 University of Waikato 31 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, 2/4/2004 University of Waikato 32 Machine Learning for Data Mining 16
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20 2/4/2004 University of Waikato 53 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 2/4/2004 University of Waikato 92 Machine Learning for Data Mining 20
21 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 2/4/2004 University of Waikato 108 2/4/2004 University of Waikato 109 Machine Learning for Data Mining 21
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25 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 2/4/2004 University of Waikato 116 2/4/2004 University of Waikato 117 Machine Learning for Data Mining 25
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29 2/4/2004 University of Waikato 124 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 2/4/2004 University of Waikato 125 Machine Learning for Data Mining 29
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36 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! 2/4/2004 University of Waikato 138 The Knowledge Flow GUI New graphical user interface for WEKA Java-Beans-based interface for setting up and running machine learning experiments Data sources, classifiers, etc. are beans and can be connected graphically Data flows through components: e.g., data source -> filter -> classifier -> evaluator Layouts can be saved and loaded again later 2/4/2004 University of Waikato 152 Machine Learning for Data Mining 36
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47 Conclusion: try it yourself! WEKA is available at 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 2/4/2004 University of Waikato 173 Machine Learning for Data Mining 47
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