AutoTrainer

abstract def copy(extra: ParamMap): Estimator[TrainedModel]

abstract def fit(dataset: Dataset[_]): TrainedModel

abstract def modelDoc: String

Doc for model to run.

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

def additionalPythonMethods(): String

final def asInstanceOf[T0]: T0

final def clear(param: Param[_]): AutoTrainer.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

val featuresCol: Param[String]

The name of the features column

def finalize(): Unit

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

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

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

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

final def getClass(): Class[_]

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

def getFeaturesCol: String

def getLabelCol: String

def getModel: Estimator[_ <: Model[_]]

def getNumFeatures: Int

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

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

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

def hasParam(paramName: String): 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

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

def isTraceEnabled(): Boolean

val labelCol: Param[String]

The name of the label column

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

val model: EstimatorParam

Model to run.

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numFeatures: IntParam

Number of features to hash to

lazy val params: Array[Param[_]]

def save(path: String): Unit

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

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

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

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

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

def setFeaturesCol(value: String): AutoTrainer.this.type

def setLabelCol(value: String): AutoTrainer.this.type

def setModel(value: Estimator[_ <: Model[_]]): AutoTrainer.this.type

def setNumFeatures(value: Int): AutoTrainer.this.type

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

def toString(): String

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: MLWriter