CombinedModel

abstract def createPredictionMetadata(): Metadata

abstract def descriminantColumn: Param[String]

abstract def directTransform(data: DataFrame): Option[Column]

abstract def indirectTransform(dataset: DataFrame): DataFrame

In case if direct transformation is not possible, applies less efficientt indirect logic.

abstract def transformSchema(schema: StructType): StructType

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

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

def clone(): AnyRef

def copy(summary: ModelSummary, params: ParamMap): C

def copy(blocks: Map[Block, DataFrame], params: ParamMap = ParamMap()): C

def copy(extra: ParamMap): C

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

def create(): C

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

def disableSaveSummary(): ModelWithSummary[C]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def escalateBlocks(): Unit

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

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def finalize(): Unit

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

final def getClass(): Class[_]

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

def getDescriminantColumn: String

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

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

final def getPredictionCol: String

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

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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

final def isInstanceOf[T0]: Boolean

def isSaveSummaryEnabled: Boolean

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

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

def mayBePropagateKey(data: DataFrame, key: Any): DataFrame

final def ne(arg0: AnyRef): Boolean

def nested: Map[String, M]

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

var parent: Estimator[C]

final val predictionCol: Param[String]

final val propagatedKeyColumn: Param[String]

def save(path: String): Unit

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

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

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

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

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

def setParent(parent: Estimator[C]): C

def setPredictionCol(value: String): CombinedModel.this.type

def setPropagatedKeyColumn(value: String): CombinedModel.this.type

def summary: ModelSummary

val summaryParam: Param[ModelSummary]

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

def toString(): String

def transform(dataset: Dataset[_]): DataFrame

def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

def transformSchema(schema: StructType, logging: Boolean): StructType

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def write: WithSummaryWriter[C]