org.apache.spark.mllib.evaluation

Class BinaryClassificationMetrics

Object

org.apache.spark.mllib.evaluation.BinaryClassificationMetrics

All Implemented Interfaces:

Logging

public class BinaryClassificationMetrics
extends Object
implements Logging

:: Experimental :: Evaluator for binary classification.

Constructor Summary

Constructors

Constructor and Description
BinaryClassificationMetrics(RDD<scala.Tuple2<Object,Object>> scoreAndLabels)

Method Summary

Methods

Modifier and Type	Method and Description
double	areaUnderPR() Computes the area under the precision-recall curve.
double	areaUnderROC() Computes the area under the receiver operating characteristic (ROC) curve.
RDD<scala.Tuple2<Object,Object>>	fMeasureByThreshold() Returns the (threshold, F-Measure) curve with beta = 1.0.
RDD<scala.Tuple2<Object,Object>>	fMeasureByThreshold(double beta) Returns the (threshold, F-Measure) curve.
RDD<scala.Tuple2<Object,Object>>	pr() Returns the precision-recall curve, which is an RDD of (recall, precision), NOT (precision, recall), with (0.0, 1.0) prepended to it.
RDD<scala.Tuple2<Object,Object>>	precisionByThreshold() Returns the (threshold, precision) curve.
RDD<scala.Tuple2<Object,Object>>	recallByThreshold() Returns the (threshold, recall) curve.
RDD<scala.Tuple2<Object,Object>>	roc() Returns the receiver operating characteristic (ROC) curve, which is an RDD of (false positive rate, true positive rate) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
RDD<Object>	thresholds() Returns thresholds in descending order.
void	unpersist() Unpersist intermediate RDDs used in the computation.

Methods inherited from class Object

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

Methods inherited from interface org.apache.spark.Logging

initialized, initializeIfNecessary, initializeLogging, initLock, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logTrace, logTrace, logWarning, logWarning

Constructor Detail

BinaryClassificationMetrics

public BinaryClassificationMetrics(RDD<scala.Tuple2<Object,Object>> scoreAndLabels)

Method Detail

unpersist

public void unpersist()

Unpersist intermediate RDDs used in the computation.

thresholds

public RDD<Object> thresholds()

Returns thresholds in descending order.

roc

public RDD<scala.Tuple2<Object,Object>> roc()

Returns the receiver operating characteristic (ROC) curve, which is an RDD of (false positive rate, true positive rate) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

See Also:

http://en.wikipedia.org/wiki/Receiver_operating_characteristic

areaUnderROC

public double areaUnderROC()

Computes the area under the receiver operating characteristic (ROC) curve.

pr

public RDD<scala.Tuple2<Object,Object>> pr()

Returns the precision-recall curve, which is an RDD of (recall, precision), NOT (precision, recall), with (0.0, 1.0) prepended to it.

See Also:

http://en.wikipedia.org/wiki/Precision_and_recall

areaUnderPR

public double areaUnderPR()

Computes the area under the precision-recall curve.

fMeasureByThreshold

public RDD<scala.Tuple2<Object,Object>> fMeasureByThreshold(double beta)

Returns the (threshold, F-Measure) curve.

Parameters:

beta - the beta factor in F-Measure computation.

Returns:

an RDD of (threshold, F-Measure) pairs.

See Also:

http://en.wikipedia.org/wiki/F1_score

fMeasureByThreshold

public RDD<scala.Tuple2<Object,Object>> fMeasureByThreshold()

Returns the (threshold, F-Measure) curve with beta = 1.0.

precisionByThreshold

public RDD<scala.Tuple2<Object,Object>> precisionByThreshold()

Returns the (threshold, precision) curve.

recallByThreshold

public RDD<scala.Tuple2<Object,Object>> recallByThreshold()

Returns the (threshold, recall) curve.