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Class weka.classifiers.evaluation.EvaluationUtils
java.lang.Object
|
+----weka.classifiers.evaluation.EvaluationUtils
- public class EvaluationUtils
- extends java.lang.Object
Contains utility functions for generating lists of predictions in
various manners.
- Version:
- $Revision: 1.6 $
- Author:
- Len Trigg (len@intelligenesis.net)
EvaluationUtils()
-
getCVPredictions(DistributionClassifier, Instances, int)
- Generate a bunch of predictions ready for processing, by performing a
cross-validation on the supplied dataset.
getPrediction(DistributionClassifier, Instance)
- Generate a single prediction for a test instance given the pre-trained
classifier.
getSeed()
- Gets the seed for randomization during cross-validation
getTestPredictions(DistributionClassifier, Instances)
- Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set assuming the classifier is already trained.
getTrainTestPredictions(DistributionClassifier, Instances, Instances)
- Generate a bunch of predictions ready for processing, by performing a
evaluation on a test set after training on the given training set.
setSeed(int)
- Sets the seed for randomization during cross-validation
EvaluationUtils
public EvaluationUtils()
setSeed
public void setSeed(int seed)
Sets the seed for randomization during cross-validation
getSeed
public int getSeed()
Gets the seed for randomization during cross-validation
getCVPredictions
public FastVector getCVPredictions(DistributionClassifier 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 DistributionClassifier to evaluate
data
- the dataset
numFolds
- the number of folds in the cross-validation.
- Throws:
- java.lang.Exception - if an error occurs
getTrainTestPredictions
public FastVector getTrainTestPredictions(DistributionClassifier 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 DistributionClassifier to evaluate
train
- the training dataset
test
- the test dataset
- Throws:
- java.lang.Exception - if an error occurs
getTestPredictions
public FastVector getTestPredictions(DistributionClassifier 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 DistributionClassifier to evaluate
test
- the test dataset
- Throws:
- java.lang.Exception - if an error occurs
getPrediction
public Prediction getPrediction(DistributionClassifier 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 DistributionClassifier to evaluate
test
- the test instance
- Throws:
- java.lang.Exception - if an error occurs
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