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Machine Learning with Weka

Machine Learning with Weka. Cornelia Caragea. Thanks to Eibe Frank for some of the slides. Weka: A Machine Learning Toolkit Preparing Data Building Classifiers. Outline. WEKA: the software. Machine learning/data mining software written in Java (distributed under the GNU Public License)

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Machine Learning with Weka

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  1. Machine Learning with Weka Cornelia Caragea Thanks to Eibe Frank for some of the slides

  2. Weka: A Machine Learning Toolkit Preparing Data Building Classifiers Outline

  3. 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

  4. 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

  5. WEKA: resources API Documentation, Tutorials, Source code. WEKA mailing list Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Weka-related Projects: Weka-Parallel - parallel processing for Weka RWeka - linking R and Weka YALE - Yet Another Learning Environment Many others…

  6. Weka: web site http://www.cs.waikato.ac.nz/ml/weka/

  7. WEKA: launching java -jar weka.jar

  8. Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers

  9. @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 Flat file in ARFF format

  10. @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 numeric attribute nominal attribute

  11. Explorer: pre-processing the data Data can be imported from a file in various formats: ARFF, CSV, 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, …

  12. Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers

  13. Explorer: building “classifiers” Classifiers in WEKA are models for predicting nominal or numeric quantities Implemented learning schemes include: Decision trees, support vector machines, perceptrons, neural networks, logistic regression, Bayes nets, … “Meta”-classifiers include: Bagging, boosting, stacking, …

  14. Outline Machine Learning Software Preparing Data Building Classifiers

  15. To Do Try Naïve Bayes and Logistic Regression classifiers on a different Weka dataset Use various parameters Try Linear regression

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