weka: experimenter and knowledge flow interfaces neil mac parthaláin email: ncm@aber.ac.uk
Post on 03-Jan-2016
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What is different from Explorer?
• Experimenter is used for ‘batches’ of experiments• Can only be used for Classification and Regression
problems*• Results are generated in a different way– Explorer:
% correct = (sum of correctly classified instances for all test folds)/(Total No. of instances in dataset)
– Experimenter:% correct = Average of correctly predicted over all folds
*Possible to perform attribute selection but not covered here
Experimenter - Overview
• Compare the performance of different learning schemes easily
• Allows better analysis than Explorer• Results: write-to-file or database• Evaluation: cross-validation, learning curve, or
hold-out• Ability to iterate over different parameter
settings• Statistical significance tests “for free”!
Experimenter - Overview
• The interface essentially has three ‘panes’:
– Setup: Configure experiments
– Run: Generate results files
– Analyse: Analyse the results of the experiments
Knowledge Flow Interface
• “Visual: drag-and-drop” user interface for WEKA - intuitive
• Java-Beans-based• Can do everything that Explorer does (plus a bit more),
but not as comprehensively as Experimenter• Data sources, classifiers, etc. are beans and can be
connected graphically• Data “flows” through modules: e.g.,“data source” ->“filter” ->“classifier”-> “evaluator”• KF layouts can be saved and re-used later
Knowledge Flow: An Example
• What we want to do:– Take a dataset– Do some attribute selection– Perform some classification on the reduced data
using 10 fold CV– Examine the subsets selected for each CV fold– Visualise the results in text format and ROC
TextViewers can be used for visualisation of results as well as examining the processes – more later…
Some problems you may encounter…
Often caused by incorrectly defined .arff files… - too many attribs defined in the header - Incorrectly labelled @attribute types
Be aware also that WEKA labels the dataset by whatever name you put in the @Relation field!
Some problems you may encounter…
You may experience an error related to Java heap size if:- The initial heap size is too-small- You load a large dataset- Attempt to run a large number of experiments
Can be fixed by initialising the JVM with a large initial heap size:Java –Xmx2048m ...
Write your own algorithms…
• WEKA is Open Source!• Much of the work is already done for you• Take advantage of the WEKA framework• Writing code and contributing to the WEKA
project now easier than before see:http://weka.wikispaces.com/How+can+I+contribute+to+WEKA%3F
Conclusion
• Experimenter and Knowledge Flow: – offer useful and flexible ways to perform a range of
batches of experiments– Beware of the way in which results are generated!– KF is particularly useful for visualisation – Experimenter more suited to learning
• Just a snapshot of capabilities of WEKA!• Want more info? email me (or Richard)• These slides available at:
http://users.aber.ac.uk/ncm/weka_slides
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