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Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression.

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Presentation on theme: "Department of Computer Science, University of Waikato, New Zealand Eibe Frank WEKA: A Machine Learning Toolkit The Explorer Classification and Regression."— Presentation transcript:

1 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 - a reminder (?) based on notes by

2 10/25/2015University of Waikato2 WEKA: the bird Copyright: Martin Kramer (mkramer@wxs.nl)

3 10/25/2015University of Waikato3 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

4 10/25/2015University of Waikato4 WEKA: versions There are several versions of WEKA:  WEKA 3.0: “book version” compatible with description in data mining book 1 st 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

5 10/25/2015University of Waikato5 @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

6 10/25/2015University of Waikato6 @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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10 10/25/2015University of Waikato10 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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18 10/25/2015University of Waikato18 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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53 10/25/2015University of Waikato53 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 3 rd -party add-ons: sourceforge.net http://www.cs.waikato.ac.nz/ml/weka

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59 @relation ukus @attribute center numeric @attribute centre numeric @attribute centerpercent numeric @attribute color numeric @attribute colour numeric @attribute colorpercent numeric @attribute english {UK,US} @data 1,32,3, 0,20,0, UK 0,25,0, 0,12,0, UK 9,27,25, 0,84,0, UK 0,19,0, 0,24,0, UK 0,16,0, 0,14,0, UK 0,16,0, 0,12,0, UK 0,21,0, 0,38,0, UK 0,25,0, 0,34,0, UK 2,26,7, 2,3,40, UK 2,32,5, 1,59,2, UK 31,0,100, 55,0,100, US 61,0,100, 26,0,100, US 24,0,100, 11,0,100, US 12,1,92, 21,4,84, US 8,0,100, 4,2,67, US 10,0,100, 8,0,100, US 19,0,100, 22,0,100, US 14,0,100, 7,0,100, US 14,0,100, 6,0,100, US 8,5,62, 24,0,100, US

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71 @relation test @attribute center numeric @attribute centre numeric @attribute centerpercent numeric @attribute color numeric @attribute colour numeric @attribute colorpercent numeric @attribute english {UK,US} @data 10,5,33, 0,20,0, UK

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