BinaryClassificationMetricsSpark

Instance Constructors

new BinaryClassificationMetricsSpark(scores: RDD[(Double, Double)], len: Long, minmax: (Double, Double))

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
def accuracyByThreshold(): List[(Double, Double)]
def areaUnderPR(): Double

Calculate the area under the Precision-Recall curve.
def areaUnderROC(): Double

Calculate the area under the Receiver Operating Characteristic curve.
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

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

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

Definition Classes
AnyRef → Any
def fMeasureByThreshold(beta: Double): List[(Double, Double)]

Calculate the F1 metric by threshold, for an arbitrary beta value
def fMeasureByThreshold(): List[(Double, Double)]

Calculate the F1 metric by threshold, for a beta value of 1.0
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def generatePlots(): Unit

Generate the PR, ROC and F1 measure plots using Scala-Chart.
Generate the PR, ROC and F1 measure plots using Scala-Chart.

Definition Classes
BinaryClassificationMetricsSpark → Metrics
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

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

Definition Classes
Any
def kpi(): DenseVector[Double]

Definition Classes
BinaryClassificationMetricsSpark → Metrics
val len: Long
val length: Long
var name: String

Attributes
protected
Definition Classes
Metrics
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def pr(): List[(Double, Double)]

Return the Precision-Recall curve, as a List of Tuple2 (Recall, Precision).
def precisionByThreshold(): List[(Double, Double)]

Return the Precision-Threshold curve, as a List of Tuple2 (Threshold, Precision).
def print(): Unit

Definition Classes
BinaryClassificationMetricsSpark → Metrics
def recallByThreshold(): List[(Double, Double)]

Return the Recall-Threshold curve, as a List of Tuple2 (Threshold, Recall).
def roc(): List[(Double, Double)]

Return the Receiver Operating Characteristic curve, as a List of Tuple2 (False Positive Rate, True Positive Rate).
val scores: RDD[(Double, Double)]

Attributes
protected
val scoresAndLabels: List[Nothing]

Attributes
protected
Definition Classes
BinaryClassificationMetricsSpark → Metrics
def scores_and_labels: List[Nothing]
def setName(n: String): BinaryClassificationMetricsSpark.this.type

Definition Classes
Metrics
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def tpfpByThreshold(): List[(Double, (Double, Double))]

Return the True Positive and False Positive Rate with respect to the threshold, as a List of Tuple2 (Threshold, (True Positive rate, False Positive Rate)).
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package evaluation

class BinaryClassificationMetricsSpark extends Metrics[Double]

Instance Constructors

new BinaryClassificationMetricsSpark(scores: RDD[(Double, Double)], len: Long, minmax: (Double, Double))

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def accuracyByThreshold(): List[(Double, Double)]

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(beta: Double): List[(Double, Double)]

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

def finalize(): Unit

def generatePlots(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def kpi(): DenseVector[Double]

val len: Long

val length: Long

var name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

def print(): Unit

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

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

val scores: RDD[(Double, Double)]

val scoresAndLabels: List[Nothing]

def scores_and_labels: List[Nothing]

def setName(n: String): BinaryClassificationMetricsSpark.this.type

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

def toString(): String

def tpfpByThreshold(): List[(Double, (Double, Double))]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Metrics[Double]

Inherited from AnyRef

Inherited from Any

Ungrouped