AbstractLogisticRegressionModel

abstract val numClasses: Int

Number of classes this model predicts.

abstract val numFeatures: Int

abstract def predictRaw(features: Vector): Vector

abstract def rawToProbabilityInPlace(raw: Vector): Vector

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def apply(features: Vector): Double

Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def inputSchema: StructType

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def outputSchema: StructType

def predict(features: Vector): Double

Predict class based on feature vector.

def predictProbabilities(features: Vector): Vector

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

def probabilityToPrediction(probability: Vector): Double

def probabilityToPredictionIndex(probability: Vector): Int

def rawToPrediction(raw: Vector): Double

def rawToProbability(raw: Vector): Vector

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

def thresholds: Option[Array[Double]]

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit