org.encog.ml.importance
Class PerturbationFeatureImportanceCalc
- java.lang.Object
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- org.encog.ml.importance.AbstractFeatureImportance
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- org.encog.ml.importance.PerturbationFeatureImportanceCalc
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- All Implemented Interfaces:
- FeatureImportance
public class PerturbationFeatureImportanceCalc extends AbstractFeatureImportance
perturbation feature encoding can be used to determine the importance of features for any type of regression or classification model, with any compatible dataset. This method works by evaluating the performance of the model when each of the input's corrisponding data is scrambled. Features that are more important will result in worse errors when their data are scrambled. Source: Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
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Constructor Summary
Constructors Constructor and Description PerturbationFeatureImportanceCalc()
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method and Description GenerateRandomgetRnd()voidperformRanking()Perform the ranking, without using a specific training set.voidperformRanking(MLDataSet theDataset)Perform the ranking, using a specific training set.voidsetRnd(GenerateRandom rnd)Set the random number generator.-
Methods inherited from class org.encog.ml.importance.AbstractFeatureImportance
getFeatures, getFeaturesSorted, getModel, init, toString
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Method Detail
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performRanking
public void performRanking()
Perform the ranking, without using a specific training set. Not all ranking algorithms support this.
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performRanking
public void performRanking(MLDataSet theDataset)
Perform the ranking, using a specific training set. Not all ranking algorithms can make use of a dataset.- Parameters:
theDataset- The dataset.
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getRnd
public GenerateRandom getRnd()
- Returns:
- The random number generator.
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setRnd
public void setRnd(GenerateRandom rnd)
Set the random number generator.- Parameters:
rnd- The random number generator.
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