LogisticRegressionTrainingSummary

abstract def featuresCol: String

Field in "predictions" which gives the features of each instance as a vector.

abstract def labelCol: String

Field in "predictions" which gives the true label of each instance.

abstract def objectiveHistory: Array[Double]

objective function (scaled loss + regularization) at each iteration.

abstract def predictions: DataFrame

Dataframe outputted by the model's transform method.

abstract def probabilityCol: String

Field in "predictions" which gives the calibrated probability of each instance as a vector.

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

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

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

def totalIterations: Int

Number of training iterations until termination

final def wait(): Unit

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

final def wait(arg0: Long): Unit