Package moa.learners.featureanalysis
Interface FeatureImportanceClassifier
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- All Superinterfaces:
AWTRenderable
,Classifier
,Configurable
,Learner<Example<Instance>>
,MOAObject
,OptionHandler
,Serializable
- All Known Implementing Classes:
FeatureImportanceHoeffdingTree
,FeatureImportanceHoeffdingTreeEnsemble
public interface FeatureImportanceClassifier extends Classifier
Feature Importance ClassifierThis interface defines the methods to be implemented on a Classifier to allow it to produce feature importances.
See details in:
Heitor Murilo Gomes, Rodrigo Fernandes de Mello, Bernhard Pfahringer, Albert Bifet. Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. IEEE International Conference on Big Data (pp. 761-769), 2019- Author:
- Heitor Murilo Gomes
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description double[]
getFeatureImportances(boolean normalize)
Obtain the current importance for each feature.int[]
getTopKFeatures(int k, boolean normalize)
The output is a double array where values indicates the original feature index and the order of the array its ranking.-
Methods inherited from interface moa.gui.AWTRenderable
getAWTRenderer
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Methods inherited from interface moa.classifiers.Classifier
copy, correctlyClassifies, getPredictionForInstance, getSubClassifiers, getVotesForInstance, trainOnInstance
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Methods inherited from interface moa.learners.Learner
getModel, getModelContext, getModelMeasurements, getPredictionForInstance, getSublearners, getVotesForInstance, isRandomizable, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance
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Methods inherited from interface moa.MOAObject
getDescription, measureByteSize
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Methods inherited from interface moa.options.OptionHandler
getCLICreationString, getOptions, getPurposeString, prepareForUse, prepareForUse
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Method Detail
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getFeatureImportances
double[] getFeatureImportances(boolean normalize)
Obtain the current importance for each feature.- Returns:
- array containing the importance/score estimated for each feature
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getTopKFeatures
int[] getTopKFeatures(int k, boolean normalize)
The output is a double array where values indicates the original feature index and the order of the array its ranking. The size of this array is expected to be less than the complete set of features.- Parameters:
k
-normalize
-- Returns:
- the k features with the highest scores.
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