Weka explorer tutorial, Formulas and forms for Accounting. Aston University
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.21351

Weka explorer tutorial, Formulas and forms for Accounting. Aston University

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PowerPoint Presentation

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

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WEKA: the bird

Copyright: Martin Kramer ([email protected])

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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  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

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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)

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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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