Example usage for weka.core AdditionalMeasureProducer interface-usage

Introduction

In this page you can find the example usage for weka.core AdditionalMeasureProducer interface-usage.

Usage

From source file Bilbo.java

/**
 <!-- globalinfo-start -->
 * Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. <br/>
 * <br/>
 * For more information, see<br/>
 * <br/>

From source file BaggingImprove.java

/**
 *
 * @author sartikahasibuan
 */
public class BaggingImprove extends RandomizableIteratedSingleClassifierEnhancer
        implements WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler {

From source file REPTree.java

/**
 <!-- globalinfo-start -->
 * Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting).  Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).
 * <p/>
 <!-- globalinfo-end -->
 *

From source file REPRandomTree.java

/**
 <!-- globalinfo-start -->
 * Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting).  Only sorts values for numeric attributes once. Missing values are dealt with by splitting the corresponding instances into pieces (i.e. as in C4.5).
 * <p/>
 <!-- globalinfo-end -->
 *

From source file com.tum.classifiertest.FastRandomForest.java

/**
 * Based on the "weka.classifiers.trees.RandomForest" class, revision 1.12,
 * by Richard Kirkby, with minor modifications:
 * <p/>
 * - uses FastRfBagger with FastRandomTree, instead of Bagger with RandomTree.
 * - stores dataset header (instead of every Tree storing its own header)

From source file com.tum.classifiertest.FastRfBagging.java

/**
 * Based on the "weka.classifiers.meta.Bagging" class, revision 1.39,
 * by Kirkby, Frank and Trigg, with modifications:
 * <ul>
 * <p/>
 * <li>Instead of Instances, produces DataCaches; consequently, FastRfBagging

From source file com.walmart.productgenome.matching.models.EMSRandomForest.java

/**
 <!-- globalinfo-start -->
 * Class for constructing a forest of random trees.<br/>
 * <br/>
 * For more information see: <br/>
 * <br/>

From source file gyc.SMOTEBagging.java

/**
 <!-- globalinfo-start -->
 * Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. <br/>
 * <br/>
 * For more information, see<br/>
 * <br/>

From source file hr.irb.fastRandomForest.FastRandomForest.java

/**
 * Based on the "weka.classifiers.trees.RandomForest" class, revision 1.12,
 * by Richard Kirkby, with minor modifications:
 * <p/>
 * - uses FastRfBagger with FastRandomTree, instead of Bagger with RandomTree.
 * - stores dataset header (instead of every Tree storing its own header)

From source file hr.irb.fastRandomForest.FastRfBagging.java

/**
 * Based on the "weka.classifiers.meta.Bagging" class, revision 1.39,
 * by Kirkby, Frank and Trigg, with modifications:
 * <ul>
 * <p/>
 * <li>Instead of Instances, produces DataCaches; consequently, FastRfBagging

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