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

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Presentation transcript:

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

Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers Interpreting Results

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

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

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…

Weka: web site

WEKA: launching java -jar weka.jar

Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers Interpreting Results

@relation 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... WEKA only deals with “flat” files

@relation 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... WEKA only deals with “flat” files

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, …

Import Datasets into WEKA

Outline Weka: A Machine Learning Toolkit Preparing Data Building Classifiers Interpreting Results

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, …

Outline Machine Learning Software Preparing Data Building Classifiers Interpreting Results

Understanding Output

Decision Tree Output (1)

Decision Tree Output (2)

To Do List Try Decision Tree, Naïve Bayes, and Logistic Regression classifiers on a different Weka dataset Use various parameters

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