Package org.apache.spark.mllib.evaluation

Class MultilabelMetrics

Object
org.apache.spark.mllib.evaluation.MultilabelMetrics
public class MultilabelMetrics extends Object
Evaluator for multilabel classification. param: predictionAndLabels an RDD of (predictions, labels) pairs, both are non-null Arrays, each with unique elements.

  • Constructor Summary

    Constructors
    Constructor
    Description
    MultilabelMetrics(RDD<scala.Tuple2<double[],double[]>> predictionAndLabels)
     

  • Method Summary

    Modifier and Type
    Method
    Description
    double
    accuracy()
    Returns accuracy
    double
    f1Measure()
    Returns document-based f1-measure averaged by the number of documents
    double
    f1Measure(double label)
    Returns f1-measure for a given label (category)
    double
    hammingLoss()
    Returns Hamming-loss
    double[]
    labels()
     
    double
    microF1Measure()
     
    double
    microPrecision()
     
    double
    microRecall()
     
    double
    precision()
    Returns document-based precision averaged by the number of documents
    double
    precision(double label)
    Returns precision for a given label (category)
    double
    recall()
    Returns document-based recall averaged by the number of documents
    double
    recall(double label)
    Returns recall for a given label (category)
    double
    subsetAccuracy()
    Returns subset accuracy (for equal sets of labels)

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

  • Constructor Details

    • MultilabelMetrics

      public MultilabelMetrics(RDD<scala.Tuple2<double[],double[]>> predictionAndLabels)

  • Method Details

    • accuracy

      public double accuracy()
      Returns accuracy
      Returns:
      (undocumented)

    • f1Measure

      public double f1Measure()
      Returns document-based f1-measure averaged by the number of documents
      Returns:
      (undocumented)

    • f1Measure

      public double f1Measure(double label)
      Returns f1-measure for a given label (category)
      Parameters:
      label - the label.
      Returns:
      (undocumented)

    • hammingLoss

      public double hammingLoss()
      Returns Hamming-loss
      Returns:
      (undocumented)

    • labels

      public double[] labels()

    • microF1Measure

      public double microF1Measure()

    • microPrecision

      public double microPrecision()

    • microRecall

      public double microRecall()

    • precision

      public double precision()
      Returns document-based precision averaged by the number of documents
      Returns:
      (undocumented)

    • precision

      public double precision(double label)
      Returns precision for a given label (category)
      Parameters:
      label - the label.
      Returns:
      (undocumented)

    • recall

      public double recall()
      Returns document-based recall averaged by the number of documents
      Returns:
      (undocumented)

    • recall

      public double recall(double label)
      Returns recall for a given label (category)
      Parameters:
      label - the label.
      Returns:
      (undocumented)

    • subsetAccuracy

      public double subsetAccuracy()
      Returns subset accuracy (for equal sets of labels)
      Returns:
      (undocumented)