Package weka.classifiers.evaluation
Class EvaluationUtils
- java.lang.Object
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- weka.classifiers.evaluation.EvaluationUtils
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- All Implemented Interfaces:
RevisionHandler
public class EvaluationUtils extends java.lang.Object implements RevisionHandler
Contains utility functions for generating lists of predictions in various manners.- Version:
- $Revision: 1.11 $
- Author:
- Len Trigg (len@reeltwo.com)
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Constructor Summary
Constructors Constructor Description EvaluationUtils()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description FastVector
getCVPredictions(Classifier classifier, Instances data, int numFolds)
Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.Prediction
getPrediction(Classifier classifier, Instance test)
Generate a single prediction for a test instance given the pre-trained classifier.java.lang.String
getRevision()
Returns the revision string.int
getSeed()
Gets the seed for randomization during cross-validationFastVector
getTestPredictions(Classifier classifier, Instances test)
Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.FastVector
getTrainTestPredictions(Classifier classifier, Instances train, Instances test)
Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.void
setSeed(int seed)
Sets the seed for randomization during cross-validation
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Method Detail
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setSeed
public void setSeed(int seed)
Sets the seed for randomization during cross-validation
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getSeed
public int getSeed()
Gets the seed for randomization during cross-validation
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getCVPredictions
public FastVector getCVPredictions(Classifier classifier, Instances data, int numFolds) throws java.lang.Exception
Generate a bunch of predictions ready for processing, by performing a cross-validation on the supplied dataset.- Parameters:
classifier
- the Classifier to evaluatedata
- the datasetnumFolds
- the number of folds in the cross-validation.- Throws:
java.lang.Exception
- if an error occurs
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getTrainTestPredictions
public FastVector getTrainTestPredictions(Classifier classifier, Instances train, Instances test) throws java.lang.Exception
Generate a bunch of predictions ready for processing, by performing a evaluation on a test set after training on the given training set.- Parameters:
classifier
- the Classifier to evaluatetrain
- the training datasettest
- the test dataset- Throws:
java.lang.Exception
- if an error occurs
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getTestPredictions
public FastVector getTestPredictions(Classifier classifier, Instances test) throws java.lang.Exception
Generate a bunch of predictions ready for processing, by performing a evaluation on a test set assuming the classifier is already trained.- Parameters:
classifier
- the pre-trained Classifier to evaluatetest
- the test dataset- Throws:
java.lang.Exception
- if an error occurs
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getPrediction
public Prediction getPrediction(Classifier classifier, Instance test) throws java.lang.Exception
Generate a single prediction for a test instance given the pre-trained classifier.- Parameters:
classifier
- the pre-trained Classifier to evaluatetest
- the test instance- Throws:
java.lang.Exception
- if an error occurs
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Returns:
- the revision
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