OpGBTClassifier

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 val cacheNodeIds: BooleanParam

final def checkInputLength(features: Array[_]): Boolean

Checks the input length

def checkSerializable: Try[Unit]

Check if the stage is serializable

final val checkpointInterval: IntParam

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

def clone(): AnyRef

final def copy(extra: ParamMap): OpGBTClassifier.this.type

This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

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 featureSubsetStrategy: Param[String]

final val featuresCol: Param[String]

def finalize(): Unit

def fit(dataset: Dataset[_]): OpPredictorWrapperModel[GBTClassificationModel]

Function that fits the binary model

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

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

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

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

final def getCacheNodeIds: Boolean

final def getCheckpointInterval: Int

final def getClass(): Class[_]

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

final def getFeatureSubsetStrategy: String

final def getFeaturesCol: String

final def getImpurity: String

def getInputColParamNames(): Array[String]

Gets names of parameters that control input columns for Spark stage

final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

final def getInputFeatures(): Array[OPFeature]

Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

final def getInputSchema(): StructType

final def getLabelCol: String

def getLocalMlStage(): Option[Transformer]

Method to access the local version of stage being wrapped

def getLossType: String

final def getMaxBins: Int

final def getMaxDepth: Int

final def getMaxIter: Int

final def getMaxMemoryInMB: Int

final def getMetadata(): Metadata

final def getMinInfoGain: Double

final def getMinInstancesPerNode: Int

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

def getOutput(): FeatureLike[Prediction]

Output features that will be created by this stage

def getOutputColParamNames(): Array[String]

Gets names of parameters that control output columns for Spark stage

final def getOutputFeatureName: String

Name of output feature (i.e.

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

final def getPredictionCol: String

final def getSeed: Long

def getSparkMlStage(): Option[GBTClassifier]

Method to access the spark stage being wrapped

def getStageSavePath(): Option[String]

Gets a save path for wrapped spark stage

final def getStepSize: Double

final def getSubsamplingRate: Double

final def getTransientFeature(i: Int): Option[TransientFeature]

Gets an input feature at index i

final def getTransientFeatures(): Array[TransientFeature]

Gets the input Features

final def getValidationIndicatorCol: String

final def getValidationTol: Double

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

final val impurity: Param[String]

final def in1: TransientFeature

final def in2: TransientFeature

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

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def inputAsArray(in: InputFeatures): Array[OPFeature]

Function to convert InputFeatures to an Array of FeatureLike

val inputParam1Name: String

val inputParam2Name: String

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

final def isInstanceOf[T0]: Boolean

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

def isTraceEnabled(): Boolean

final val labelCol: Param[String]

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 lossType: Param[String]

final val maxBins: IntParam

final val maxDepth: IntParam

final val maxIter: IntParam

final val maxMemoryInMB: IntParam

final val minInfoGain: DoubleParam

final val minInstancesPerNode: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def onGetMetadata(): Unit

Function to be called on getMetadata

def onSetInput(): Unit

Function to be called on setInput

val operationName: String

Short unique name of the operation this stage performs

final def outputAsArray(out: OutputFeatures): Array[OPFeature]

Function to convert OutputFeatures to an Array of FeatureLike

def outputFeatureUid: String

def outputIsResponse: Boolean

Should output feature be a response? Yes, if any of the input features are.

val outputParamName: String

lazy val params: Array[Param[_]]

final val predictionCol: Param[String]

val predictor: GBTClassifier

the predictor to wrap

def save(path: String): Unit

final val seed: LongParam

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

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

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

def setCacheNodeIds(value: Boolean): OpGBTClassifier.this.type

def setCheckpointInterval(value: Int): OpGBTClassifier.this.type

Specifies how often to checkpoint the cached node IDs.

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

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

def setImpurity(value: String): OpGBTClassifier.this.type

The impurity setting is ignored for GBT models.

final def setInput(features: InputFeatures): OpGBTClassifier.this.type

Input features that will be used by the stage

final def setInputFeatures[S <: OPFeature](features: Array[S]): OpGBTClassifier.this.type

Sets input features

def setLossType(value: String): OpGBTClassifier.this.type

def setMaxBins(value: Int): OpGBTClassifier.this.type

def setMaxDepth(value: Int): OpGBTClassifier.this.type

def setMaxIter(value: Int): OpGBTClassifier.this.type

def setMaxMemoryInMB(value: Int): OpGBTClassifier.this.type

final def setMetadata(m: Metadata): OpGBTClassifier.this.type

def setMinInfoGain(value: Double): OpGBTClassifier.this.type

def setMinInstancesPerNode(value: Int): OpGBTClassifier.this.type

def setOutputDF(df: DataFrame): Unit

def setOutputFeatureName(name: String): OpGBTClassifier.this.type

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

def setSparkMlStage(stage: Option[GBTClassifier]): OpGBTClassifier.this.type

def setStageSavePath(path: String): OpGBTClassifier.this.type

Sets a save path for wrapped spark stage

def setStepSize(value: Double): OpGBTClassifier.this.type

def setSubsamplingRate(value: Double): OpGBTClassifier.this.type

final val sparkInputColParamNames: StringArrayParam

final val sparkMlStage: SparkStageParam[GBTClassifier]

final val sparkOutputColParamNames: StringArrayParam

final def stageName: String

Stage unique name consisting of the stage operation name and uid

final val stepSize: DoubleParam

final val subsamplingRate: DoubleParam

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

def toString(): String

final def transformSchema(schema: StructType): StructType

This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

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

implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

Type tag of the output

implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

Type tag of the output value

val uid: String

stage uid

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

final val validationIndicatorCol: Param[String]

final val validationTol: DoubleParam

final def wait(): Unit

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

final def wait(arg0: Long): Unit

final def write: MLWriter

def setFeatureSubsetStrategy(value: String): OpGBTClassifier.this.type