Documentation of 'jsat.classifiers.boosting.ModestAdaBoost' Java class
ModestAdaBoost
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jsat.classifiers.boosting

Class ModestAdaBoost

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, BinaryScoreClassifier, Classifier, Parameterized

    public class ModestAdaBoost
extends java.lang.Object
implements Classifier, Parameterized, BinaryScoreClassifier
    Modest Ada Boost is a generalization of Discrete Ada Boost that attempts to reduce the generalization error and avoid over-fitting. Empirically, ModestBoost usually maintains a higher training-set error, and may take more iterations to obtain the same test set error as other algorithms, but doesn't not increase as much after it reaches the minimum error - which should make it easier to obtain the higher accuracy.
    See:
    Vezhnevets, A.,&Vezhnevets, V. (2005). “Modest AdaBoost” – Teaching AdaBoost to Generalize Better. GraphiCon. Novosibirsk Akademgorodok, Russia. Retrieved from here
    See Also:
    Serialized Form

    • Constructor Detail

      • ModestAdaBoost

        public ModestAdaBoost(Classifier weakLearner,
                      int maxIterations)
        Creates a new ModestBoost learner
        Parameters:
        weakLearner - the weak learner to use
        maxIterations - the maximum number of boosting iterations

    • Method Detail

      • getModels

        public java.util.List<Classifier> getModels()
        Returns:
        a list of the models that are in this ensemble.

      • getModelWeights

        public java.util.List<java.lang.Double> getModelWeights()
        Returns:
        a list of the models weights that are in this ensemble.

      • getMaxIterations

        public int getMaxIterations()
        Returns the maximum number of iterations used
        Returns:
        the maximum number of iterations used

      • setMaxIterations

        public void setMaxIterations(int maxIterations)
        Sets the maximal number of boosting iterations that may be performed
        Parameters:
        maxIterations - the maximum number of iterations

      • getWeakLearner

        public Classifier getWeakLearner()
        Returns the weak learner currently being used by this method.
        Returns:
        the weak learner currently being used by this method.

      • setWeakLearner

        public void setWeakLearner(Classifier weakLearner)
        Sets the weak learner used during training.
        Parameters:
        weakLearner - the weak learner to use

      • getScore

        public double getScore(DataPoint dp)
        Description copied from interface: BinaryScoreClassifier
        Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
        Specified by:
        getScore in interface BinaryScoreClassifier
        Parameters:
        dp - the data point to predict the class label of
        Returns:
        the score for the given data point

      • classify

        public CategoricalResults classify(DataPoint data)
        Description copied from interface: Classifier
        Performs classification on the given data point.
        Specified by:
        classify in interface Classifier
        Parameters:
        data - the data point to classify
        Returns:
        the results of the classification.

      • train

        public void train(ClassificationDataSet dataSet,
                  boolean parallel)
        Description copied from interface: Classifier
        Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
        Specified by:
        train in interface Classifier
        Parameters:
        dataSet - the data set to train on
        parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.

      • supportsWeightedData

        public boolean supportsWeightedData()
        Description copied from interface: Classifier
        Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
        Specified by:
        supportsWeightedData in interface Classifier
        Returns:
        true if the model supports weighted data, false otherwise
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