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

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

Method Summary

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() Returns the sequence of labels in ascending order
double	microF1Measure() Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)
double	microPrecision() Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)
double	microRecall() Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)
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 Object

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

Constructor Detail

MultilabelMetrics

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

Method Detail

subsetAccuracy

public double subsetAccuracy()

Returns subset accuracy (for equal sets of labels)

Returns:

(undocumented)

accuracy

public double accuracy()

Returns accuracy

Returns:

(undocumented)

hammingLoss

public double hammingLoss()

Returns Hamming-loss

Returns:

(undocumented)

precision

public double precision()

Returns document-based precision averaged by the number of documents

Returns:

(undocumented)

recall

public double recall()

Returns document-based recall averaged by the number of documents

Returns:

(undocumented)

f1Measure

public double f1Measure()

Returns document-based f1-measure 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(double label)

Returns recall for a given label (category)

Parameters:

label - the label.

Returns:

(undocumented)

f1Measure

public double f1Measure(double label)

Returns f1-measure for a given label (category)

Parameters:

label - the label.

Returns:

(undocumented)

microPrecision

public double microPrecision()

Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)

Returns:

(undocumented)

microRecall

public double microRecall()

Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)

Returns:

(undocumented)

microF1Measure

public double microF1Measure()

Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)

Returns:

(undocumented)

labels

public double[] labels()

Returns the sequence of labels in ascending order

Returns:

(undocumented)