OptimizableLabelEstimator

abstract val default: LabelEstimator[A, B, L]

abstract def optimize(sample: RDD[A], sampleLabels: RDD[L], numPerPartition: Map[Int, Int]): LabelEstimator[A, B, L]

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 execute(deps: Seq[Expression]): TransformerExpression

def finalize(): Unit

def fit(data: RDD[A], labels: RDD[L]): Transformer[A, B]

The type-safe method that ML developers need to implement when writing new Estimators.

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def label: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

final def withData(data: PipelineDataset[A], labels: PipelineDataset[L]): Pipeline[A, B]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

final def withData(data: RDD[A], labels: RDD[L]): Pipeline[A, B]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

final def withData(data: PipelineDataset[A], labels: RDD[L]): Pipeline[A, B]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

final def withData(data: RDD[A], labels: PipelineDataset[L]): Pipeline[A, B]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.