BaggingRegressorParams

abstract def copy(extra: ParamMap): Params

abstract val uid: String

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val baseLearner: Param[EnsemblePredictorType]

param for the estimator that will be used by the ensemble learner as a base learner

final def clear(param: Param[_]): BaggingRegressorParams.this.type

def clone(): AnyRef

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: Param[String]

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

def getBaseLearner: EnsemblePredictorType

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

final def getFeaturesCol: String

final def getLabelCol: String

final def getMaxIter: Int

final def getOrDefault[T](param: Param[T]): T

def getParallelism: Int

def getParam(paramName: String): Param[Any]

final def getPredictionCol: String

def getReplacement: Boolean

def getReplacementFeatures: Boolean

def getSampleRatio: Double

def getSampleRatioFeatures: Double

final def getSeed: Long

final def getWeightCol: String

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hashCode(): Int

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

final val labelCol: Param[String]

final val maxIter: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val parallelism: IntParam

lazy val params: Array[Param[_]]

final val predictionCol: Param[String]

val replacement: Param[Boolean]

param for whether samples are drawn with replacement

val replacementFeatures: Param[Boolean]

param for whether samples are drawn with replacement

val sampleRatio: Param[Double]

param for ratio of rows sampled out of the dataset

val sampleRatioFeatures: Param[Double]

param for ratio of rows sampled out of the dataset

final val seed: LongParam

final def set(paramPair: ParamPair[_]): BaggingRegressorParams.this.type

final def set(param: String, value: Any): BaggingRegressorParams.this.type

final def set[T](param: Param[T], value: T): BaggingRegressorParams.this.type

final def setDefault(paramPairs: ParamPair[_]*): BaggingRegressorParams.this.type

final def setDefault[T](param: Param[T], value: T): BaggingRegressorParams.this.type

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

def toString(): String

def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

final def wait(): Unit

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

final def wait(arg0: Long): Unit

final val weightCol: Param[String]