Documentation of 'org.encog.ml.importance.PerturbationFeatureImportanceCalc' Java class
PerturbationFeatureImportanceCalc
org.encog.ml.importance

Class PerturbationFeatureImportanceCalc

  • 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.
    • Constructor Detail

      • PerturbationFeatureImportanceCalc

        public PerturbationFeatureImportanceCalc()
    • Method Detail

      • performRanking

        public void performRanking()
        Perform the ranking, without using a specific training set. Not all ranking algorithms support this.
      • 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.
      • getRnd

        public GenerateRandom getRnd()
        Returns:
        The random number generator.
      • setRnd

        public void setRnd(GenerateRandom rnd)
        Set the random number generator.
        Parameters:
        rnd - The random number generator.

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