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Class weka.classifiers.evaluation.EvaluationUtils
java.lang.Object
|
+----weka.classifiers.evaluation.EvaluationUtils
- public class EvaluationUtils
- extends Object
Contains utility functions for generating lists of predictions in
various manners.
- 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 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: Exception
- if an error occurs
getTrainTestPredictions
public FastVector getTrainTestPredictions(DistributionClassifier classifier,
Instances train,
Instances test) throws 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: Exception
- if an error occurs
getTestPredictions
public FastVector getTestPredictions(DistributionClassifier classifier,
Instances test) throws 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: Exception
- if an error occurs
getPrediction
public Prediction getPrediction(DistributionClassifier classifier,
Instance test) throws 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: Exception
- if an error occurs
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