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An Introduction to Weka(cont .)

An Introduction to Weka(cont .). CS IT 5210 2013, Fall. Weka Experimenter. Experimenter: compare learning schemes on datasets Evaluation options: cross-validation, learning curve, hold-out Can also iterate over different parameter settings. Choose comparison field. Weka KnowledgeFlow.

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An Introduction to Weka(cont .)

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  1. An Introduction to Weka(cont.) CSIT 5210 2013, Fall

  2. Weka Experimenter • Experimenter: compare learning schemes on datasets • Evaluation options: cross-validation, learning curve, hold-out • Can also iterate over different parameter settings

  3. Choose comparison field

  4. WekaKnowledgeFlow • New graphical user interface for WEKA • Java-Beans-based interface for setting up and running machine learning experiments • Data sources, classifiers, etc. are beans and can be connected graphically • Data “flows” through components: e.g., “data source” -> “filter” -> “classifier” -> “evaluator” • Layouts can be saved and loaded again later

  5. Right click on the ArffLoader and connect them with datasets

  6. Add TrainTestSplitMaker to make train/test set

  7. Add classifier and connect them with train/test

  8. Add classifier performance evaluator

  9. Add text viewer to view result

  10. Setting up is done, right click ArffLoader ‘configure” to choose files

  11. Right click and choose “configure” to assign the label attribute

  12. Click “run this flow” button to begin experiment

  13. right click text viewer ‘show result’ to view result.

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