TrainValidationSplit

final def !=(arg0: Any): Boolean

final def ##(): Int

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

An alias for getOrDefault().

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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

Clears the user-supplied value for the input param.

def clone(): AnyRef

def copy(extra: ParamMap): TrainValidationSplit

Creates a copy of this instance with the same UID and some extra params.

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

Copies param values from this instance to another instance for params shared by them.

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

Default implementation of copy with extra params.

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val estimator: Param[Estimator[_]]

param for the estimator to be validated

val estimatorParamMaps: Param[Array[ParamMap]]

param for estimator param maps

val evaluator: Param[Evaluator]

param for the evaluator used to select hyper-parameters that maximize the validated metric

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

Explains a param.

def explainParams(): String

Explains all params of this instance.

final def extractParamMap(): ParamMap

extractParamMap with no extra values.

final def extractParamMap(extra: ParamMap): ParamMap

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

def finalize(): Unit

def fit(dataset: Dataset[_]): TrainValidationSplitModel

Fits a model to the input data.

def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[TrainValidationSplitModel]

Fits multiple models to the input data with multiple sets of parameters.

def fit(dataset: Dataset[_], paramMap: ParamMap): TrainValidationSplitModel

Fits a single model to the input data with provided parameter map.

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainValidationSplitModel

Fits a single model to the input data with optional parameters.

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

Optionally returns the user-supplied value of a param.

final def getClass(): Class[_]

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

Gets the default value of a parameter.

def getEstimator: Estimator[_]

def getEstimatorParamMaps: Array[ParamMap]

def getEvaluator: Evaluator

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

Gets the value of a param in the embedded param map or its default value.

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

Gets a param by its name.

final def getSeed: Long

def getTrainRatio: Double

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

Tests whether the input param has a default value set.

def hasParam(paramName: String): Boolean

Tests whether this instance contains a param with a given name.

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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

Checks whether a param is explicitly set or has a default value.

final def isInstanceOf[T0]: Boolean

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

Checks whether a param is explicitly set.

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

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

Returns all params sorted by their names.

def save(path: String): Unit

Saves this ML instance to the input path, a shortcut of write.save(path).

final val seed: LongParam

Param for random seed.

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

Sets a parameter in the embedded param map.

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

Sets a parameter (by name) in the embedded param map.

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

Sets a parameter in the embedded param map.

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

Sets default values for a list of params.

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

Sets a default value for a param.

def setEstimator(value: Estimator[_]): TrainValidationSplit.this.type

def setEstimatorParamMaps(value: Array[ParamMap]): TrainValidationSplit.this.type

def setEvaluator(value: Evaluator): TrainValidationSplit.this.type

def setSeed(value: Long): TrainValidationSplit.this.type

def setTrainRatio(value: Double): TrainValidationSplit.this.type

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

def toString(): String

val trainRatio: DoubleParam

Param for ratio between train and validation data.

def transformSchema(schema: StructType): StructType

:: DeveloperApi ::

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

:: DeveloperApi ::

def transformSchemaImpl(schema: StructType): StructType

val uid: String

An immutable unique ID for the object and its derivatives.

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def write: MLWriter

Returns an MLWriter instance for this ML instance.

def validateParams(): Unit

Validates parameter values stored internally.