BinaryClassificationMetrics

Instance Constructors

new BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)])

Defaults numBins to 0.
new BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)], numBins: Int)

scoreAndLabels
an RDD of (score, label) pairs.
numBins
if greater than 0, then the curves (ROC curve, PR curve) computed internally will be down-sampled to this many "bins". If 0, no down-sampling will occur. This is useful because the curve contains a point for each distinct score in the input, and this could be as large as the input itself -- millions of points or more, when thousands may be entirely sufficient to summarize the curve. After down-sampling, the curves will instead be made of approximately numBins points instead. Points are made from bins of equal numbers of consecutive points. The size of each bin is floor(scoreAndLabels.count() / numBins), which means the resulting number of bins may not exactly equal numBins. The last bin in each partition may be smaller as a result, meaning there may be an extra sample at partition boundaries.

Value Members

final def !=(arg0: AnyRef): Boolean

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: AnyRef): Boolean

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final def ==(arg0: Any): Boolean

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def areaUnderPR(): Double

Computes the area under the precision-recall curve.
def areaUnderROC(): Double

Computes the area under the receiver operating characteristic (ROC) curve.
final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def fMeasureByThreshold(): RDD[(Double, Double)]

Returns the (threshold, F-Measure) curve with beta = 1.
Returns the (threshold, F-Measure) curve with beta = 1.0.
def fMeasureByThreshold(beta: Double): RDD[(Double, Double)]

Returns the (threshold, F-Measure) curve.
Returns the (threshold, F-Measure) curve.
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
def finalize(): Unit

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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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def hashCode(): Int

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final def isInstanceOf[T0]: Boolean

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def isTraceEnabled(): Boolean

Attributes
protected
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def log: Logger

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protected
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def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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protected
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def logDebug(msg: ⇒ String): Unit

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protected
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def logError(msg: ⇒ String, throwable: Throwable): Unit

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def logError(msg: ⇒ String): Unit

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def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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def logInfo(msg: ⇒ String): Unit

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protected
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def logName: String

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def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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protected
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def logTrace(msg: ⇒ String): Unit

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protected
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def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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protected
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def logWarning(msg: ⇒ String): Unit

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protected
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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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val numBins: Int

if greater than 0, then the curves (ROC curve, PR curve) computed internally will be down-sampled to this many "bins".
if greater than 0, then the curves (ROC curve, PR curve) computed internally will be down-sampled to this many "bins". If 0, no down-sampling will occur. This is useful because the curve contains a point for each distinct score in the input, and this could be as large as the input itself -- millions of points or more, when thousands may be entirely sufficient to summarize the curve. After down-sampling, the curves will instead be made of approximately numBins points instead. Points are made from bins of equal numbers of consecutive points. The size of each bin is floor(scoreAndLabels.count() / numBins), which means the resulting number of bins may not exactly equal numBins. The last bin in each partition may be smaller as a result, meaning there may be an extra sample at partition boundaries.
def pr(): RDD[(Double, Double)]

Returns the precision-recall curve, which is an RDD of (recall, precision), NOT (precision, recall), with (0.
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
def precisionByThreshold(): RDD[(Double, Double)]

Returns the (threshold, precision) curve.
def recallByThreshold(): RDD[(Double, Double)]

Returns the (threshold, recall) curve.
def roc(): RDD[(Double, Double)]

Returns the receiver operating characteristic (ROC) curve, which is an RDD of (false positive rate, true positive rate) with (0.
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
val scoreAndLabels: RDD[(Double, Double)]

an RDD of (score, label) pairs.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
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def thresholds(): RDD[Double]

Returns thresholds in descending order.
def toString(): String

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def unpersist(): Unit

Unpersist intermediate RDDs used in the computation.
final def wait(): Unit

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@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

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final def wait(arg0: Long): Unit

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class BinaryClassificationMetrics extends Logging

Instance Constructors

new BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)])

new BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)], numBins: Int)

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def areaUnderPR(): Double

def areaUnderROC(): Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def fMeasureByThreshold(): RDD[(Double, Double)]

def fMeasureByThreshold(beta: Double): RDD[(Double, Double)]

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numBins: Int

def pr(): RDD[(Double, Double)]

def precisionByThreshold(): RDD[(Double, Double)]

def recallByThreshold(): RDD[(Double, Double)]

def roc(): RDD[(Double, Double)]

val scoreAndLabels: RDD[(Double, Double)]

final def synchronized[T0](arg0: ⇒ T0): T0

def thresholds(): RDD[Double]

def toString(): String

def unpersist(): Unit

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Logging

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