LogisticRegressionModel

Instance Constructors

new LogisticRegressionModel(coefficients: Vector, intercept: Double, threshold: Option[Double] = Some(0.5))

coefficients
coefficients vector for model
intercept
intercept of model
threshold
threshold for pegging predictions

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 apply(features: Vector): Double

Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.
Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.
features
feature vector
returns
prediction

Definition Classes
ClassificationModel
final def asInstanceOf[T0]: T0

Definition Classes
Any
def binaryProbabilityToPrediction(probability: Double): Double

Definition Classes
BinaryClassificationModel
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val coefficients: Vector

coefficients vector for model
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

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

Definition Classes
AnyRef → Any
val intercept: Double

intercept of model
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numClasses: Int

Number of classes this model predicts.
Number of classes this model predicts.
2 indicates this is a binary classification model. Greater than 2 indicates a multinomial classifier.

Definition Classes
BinaryClassificationModel → MultinomialClassificationModel
def predict(features: Vector): Double

Predict the class taking into account threshold.
Predict the class taking into account threshold.
features
features for prediction
returns
prediction with threshold

Definition Classes
BinaryClassificationModel → MultinomialClassificationModel → ClassificationModel
def predictBinaryProbability(features: Vector): Double

Predict the class without taking into account threshold.
Predict the class without taking into account threshold.
features
features for prediction
returns
probability that prediction is the predictable class

Definition Classes
LogisticRegressionModel → BinaryClassificationModel
def predictBinaryWithProbability(features: Vector): (Double, Double)

Predict class and probability.
Predict class and probability.
features
features to predict
returns
(prediction, probability)

Definition Classes
BinaryClassificationModel
def predictProbabilities(features: Vector): Vector

Definition Classes
MultinomialClassificationModel
def predictRaw(features: Vector): Vector

Definition Classes
LogisticRegressionModel → MultinomialClassificationModel
def predictWithProbability(features: Vector): (Double, Double)

Definition Classes
MultinomialClassificationModel
def probabilityToPrediction(probability: Vector): Double

Definition Classes
MultinomialClassificationModel
def rawToPrediction(raw: Vector): Double

Definition Classes
MultinomialClassificationModel
def rawToProbability(raw: Vector): Vector

Definition Classes
MultinomialClassificationModel
def rawToProbabilityInPlace(raw: Vector): Vector

Definition Classes
LogisticRegressionModel → MultinomialClassificationModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val threshold: Option[Double]

threshold for pegging predictions
threshold for pegging predictions

Definition Classes
LogisticRegressionModel → BinaryClassificationModel
lazy val thresholds: Option[Array[Double]]

Definition Classes
BinaryClassificationModel → MultinomialClassificationModel
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 classification

case class LogisticRegressionModel(coefficients: Vector, intercept: Double, threshold: Option[Double] = Some(0.5)) extends BinaryClassificationModel with Serializable with Product

Instance Constructors

new LogisticRegressionModel(coefficients: Vector, intercept: Double, threshold: Option[Double] = Some(0.5))

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def apply(features: Vector): Double

final def asInstanceOf[T0]: T0

def binaryProbabilityToPrediction(probability: Double): Double

def clone(): AnyRef

val coefficients: Vector

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

val intercept: Double

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numClasses: Int

def predict(features: Vector): Double

def predictBinaryProbability(features: Vector): Double

def predictBinaryWithProbability(features: Vector): (Double, Double)

def predictProbabilities(features: Vector): Vector

def predictRaw(features: Vector): Vector

def predictWithProbability(features: Vector): (Double, Double)

def probabilityToPrediction(probability: Vector): Double

def rawToPrediction(raw: Vector): Double

def rawToProbability(raw: Vector): Vector

def rawToProbabilityInPlace(raw: Vector): Vector

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

val threshold: Option[Double]

lazy val thresholds: Option[Array[Double]]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from Serializable

Inherited from Serializable

Inherited from BinaryClassificationModel

Inherited from MultinomialClassificationModel

Inherited from ClassificationModel

Inherited from AnyRef

Inherited from Any

Ungrouped