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Machine Learning with Weka Cornelia Caragea Thanks to Eibe Frank for some of the slides
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Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers Interpreting Results
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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 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: “book version” compatible with description in data mining book WEKA: “GUI version” adds graphical user interfaces (book version is command-line only) WEKA: “development version” with lots of improvements
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WEKA: resources API Documentation, Tutorials, Source code. API DocumentationTutorials WEKA mailing list Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Weka-related Projects: Weka-Parallel - parallel processing for Weka Weka-Parallel RWeka - linking R and Weka RWeka YALE - Yet Another Learning Environment YALE Many others…
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Weka: web site http://www.cs.waikato.ac.nz/ml/weka/
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WEKA: launching java -jar weka.jar
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Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers Interpreting Results
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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} @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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@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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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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Import Datasets into WEKA
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Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers Interpreting Results
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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, … “Meta”-classifiers include: Bagging, boosting, stacking, error-correcting output codes, locally weighted learning, …
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Outline Machine Learning Software Preparing Data Building Classifiers Interpreting Results
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Understanding Output
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Decision Tree Output (1)
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Decision Tree Output (2)
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To Do List Try Decision Tree, Naïve Bayes, and Logistic Regression classifiers on a different Weka dataset Use various parameters
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