org.encog.ml.importance

Interface FeatureImportance

All Known Implementing Classes:

AbstractFeatureImportance, CorrelationFeatureImportanceCalc, NeuralFeatureImportanceCalc, PerturbationFeatureImportanceCalc

public interface FeatureImportance

Defines an interface for classes that are used to rank the importance of the input features to a model.

Method Summary

Modifier and Type	Method and Description
java.util.List<FeatureRank>	getFeatures()
java.util.Collection<FeatureRank>	getFeaturesSorted()
MLRegression	getModel()
void	init(MLRegression theModel, java.lang.String[] names) Initialize a model
void	performRanking() Perform the ranking, without using a specific training set.
void	performRanking(MLDataSet theDataset) Perform the ranking, using a specific training set.

Method Detail

init

void init(MLRegression theModel,
          java.lang.String[] names)

Initialize a model

Parameters:

theModel - The model that will be used for ranking.

names - The names of the fields.

performRanking

void performRanking()

Perform the ranking, without using a specific training set. Not all ranking algorithms support this.

performRanking

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.

getFeatures

java.util.List<FeatureRank> getFeatures()

Returns:

The individual rankings of each feature.

getFeaturesSorted

java.util.Collection<FeatureRank> getFeaturesSorted()

Returns:

The sorted individual rankings of each feature.

getModel

MLRegression getModel()

Returns:

The model that was evaluated.