A Short Introduction to Weka

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A Short Introduction to Weka

• Natural Language Processing
• Thursday, September 25th

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What is weka?

• Java-based Machine Learning Tool
• Implements numerous classifiers
• 3 modes of operation
• GUI
• Command Line
• Java API (not discussed here)
• Google weka java

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weka Homepage

• http//www.cs.waikato.ac.nz/ml/weka/
• To run
• java -Xmx1024M -jar cs4705/bin/weka.jar

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.arff file format

• http//www.cs.waikato.ac.nz/ml/weka/arff.html
• 1. Title Iris Plants Database
• _at_RELATION iris
• _at_ATTRIBUTE sepallength NUMERIC _at_ATTRIBUTE
  sepalwidth NUMERIC _at_ATTRIBUTE petallength
  NUMERIC _at_ATTRIBUTE petalwidth NUMERIC
  _at_ATTRIBUTE class Iris-setosa,Iris-versicolor,
  Iris-virginica
• _at_DATA 5.1,3.5,1.4,0.2,Iris-setosa
  4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris
  -setosa

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.arff file format

• _at_attribute attrName numeric, string, ltnominalgt,
  date
• numeric a number
• nominal a (finite) set of strings,
  e.g.Iris-setosa,Iris-versicolor,
  Iris-virginica
• string ltarbitrary stringsgt
• date (default ISO-8601) yyyy-MM-ddTHHmmss

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Example Arff Files

• cs4705/bin/weka-3-4-11/data/
• iris.arff
• soybean.arff
• weather.arff

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To Classify with weka GUI

1. Run weka GUI
2. Click 'Explorer'
3. 'Open file...'
4. Select 'Classify' tab
5. 'Choose' a classifier
6. Confirm options

• Click 'Start'
• Wait...
• Right-click on Result list entry
• 'Save result buffer'
• 'Save model'

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Classify

• Some classifiers to start with.
• NaiveBayes
• JRip
• J48
• SMO
• Find References by selecting a classifier
• Use Cross-Validation!

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Analyzing Results

• Important tools for Homework 2
• Accuracy
• Correctly classified instances
• F-measure
• Confusion matrix
• Save model
• Visualization

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Running weka from the Command Line

• Running an N-fold cross validation experiment
• java -cp cs4705/bin/weka.jar weka.classifiers.bay
  es.NaiveBayes -t trainingdata.arff -x N -i
• Using a predefined test set
• java -cp cs4705/bin/weka.jar weka.classifiers.bay
  es.NaiveBayes -t trainingdata.arff -T
  testingdata.arff

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• Saving the model
• java -cp cs4705/bin/weka.jar weka.classifiers.bay
  es.NaiveBayes -t trainingdata.arff -d
  output.model
• Classifying a test set
• java -cp cs4705/bin/weka.jar weka.classifiers.bay
  es.NaiveBayes -l input.model -T testingdata.arff
• Getting help
• java -cp cs4705/bin/weka.jar weka.classifiers.bay
  es.NaiveBayes -?

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Homework 2 Weka Workflow

T1
TN
YourFeature Extractor
S1
S2
YourFeature Extractor
best model
Weka
.arff
Test .arff

SN
results
results
Weka
Preprocessing (you)
Grading (us)
Experimentation (you)
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Tips for Homework Success

• Start early
• Read instructions carefully
• Start simply
• Your system should always work
• 80/20 Rule
• Add features incrementally
• This way, you always have something you can turn
  in.