MultilabelMetrics

Instance Constructors

new MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])])

predictionAndLabels
an RDD of (predictions, labels) pairs, both are non-null Arrays, each with unique elements.

Annotations
@Since( "1.2.0" )

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
lazy val accuracy: Double

Returns accuracy
Returns accuracy

Annotations
@Since( "1.2.0" )
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

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

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def f1Measure(label: Double): Double

Returns f1-measure for a given label (category)
Returns f1-measure for a given label (category)
label
the label.

Annotations
@Since( "1.2.0" )
lazy val f1Measure: Double

Returns document-based f1-measure averaged by the number of documents
Returns document-based f1-measure averaged by the number of documents

Annotations
@Since( "1.2.0" )
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
lazy val hammingLoss: Double

Returns Hamming-loss
Returns Hamming-loss

Annotations
@Since( "1.2.0" )
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
lazy val labels: Array[Double]

Returns the sequence of labels in ascending order
Returns the sequence of labels in ascending order

Annotations
@Since( "1.2.0" )
lazy val microF1Measure: Double

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

Annotations
@Since( "1.2.0" )
lazy val microPrecision: Double

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

Annotations
@Since( "1.2.0" )
lazy val microRecall: Double

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

Annotations
@Since( "1.2.0" )
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def precision(label: Double): Double

Returns precision for a given label (category)
Returns precision for a given label (category)
label
the label.

Annotations
@Since( "1.2.0" )
lazy val precision: Double

Returns document-based precision averaged by the number of documents
Returns document-based precision averaged by the number of documents

Annotations
@Since( "1.2.0" )
def recall(label: Double): Double

Returns recall for a given label (category)
Returns recall for a given label (category)
label
the label.

Annotations
@Since( "1.2.0" )
lazy val recall: Double

Returns document-based recall averaged by the number of documents
Returns document-based recall averaged by the number of documents

Annotations
@Since( "1.2.0" )
lazy val subsetAccuracy: Double

Returns subset accuracy (for equal sets of labels)
Returns subset accuracy (for equal sets of labels)

Annotations
@Since( "1.2.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package evaluation

class MultilabelMetrics extends AnyRef

Instance Constructors

new MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])])

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

lazy val accuracy: Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def f1Measure(label: Double): Double

lazy val f1Measure: Double

def finalize(): Unit

final def getClass(): Class[_]

lazy val hammingLoss: Double

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

lazy val labels: Array[Double]

lazy val microF1Measure: Double

lazy val microPrecision: Double

lazy val microRecall: Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def precision(label: Double): Double

lazy val precision: Double

def recall(label: Double): Double

lazy val recall: Double

lazy val subsetAccuracy: Double

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from AnyRef

Inherited from Any

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