GBMRegressor

Instance Constructors

new GBMRegressor()
new GBMRegressor(uid: String)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val baseLearner: Param[EnsemblePredictorType]

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

Definition Classes
HasBaseLearner
final def clear(param: Param[_]): GBMRegressor.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copy(extra: ParamMap): GBMRegressor

Definition Classes
GBMRegressor → Predictor → Estimator → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

Attributes
protected
Definition Classes
Predictor
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def fit(dataset: Dataset[_]): GBMRegressionModel

Definition Classes
Predictor → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[GBMRegressionModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMap: ParamMap): GBMRegressionModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GBMRegressionModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
def getBaseLearner: EnsemblePredictorType

Definition Classes
HasBaseLearner
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getLabelCol: String

Definition Classes
HasLabelCol
def getLearningRate: Double

Definition Classes
HasLearningRate
def getLoss: String

Definition Classes
GBMRegressorParams
final def getMaxIter: Int

Definition Classes
HasMaxIter
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getParam(paramName: String): Param[Any]

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getSeed: Long

Definition Classes
HasSeed
final def getTol: Double

Definition Classes
HasTol
final def getWeightCol: String

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

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

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

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
final val labelCol: Param[String]

Definition Classes
HasLabelCol
val learningRate: Param[Double]

param for the learning rate of the algorithm
param for the learning rate of the algorithm

Definition Classes
HasLearningRate
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
val loss: Param[String]

Loss function which Boosting tries to minimize.
Loss function which Boosting tries to minimize. (case-insensitive) Supported: "ls", "lad", "huber", "quantile". (default = ls)

Definition Classes
GBMRegressorParams
final val maxIter: IntParam

Definition Classes
HasMaxIter
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
lazy val params: Array[Param[_]]

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final val seed: LongParam

Definition Classes
HasSeed
final def set(paramPair: ParamPair[_]): GBMRegressor.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): GBMRegressor.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): GBMRegressor.this.type

Definition Classes
Params
def setBaseLearner(value: Predictor[_, _, _]): GBMRegressor.this.type
final def setDefault(paramPairs: ParamPair[_]*): GBMRegressor.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): GBMRegressor.this.type

Attributes
protected
Definition Classes
Params
def setFeaturesCol(value: String): GBMRegressor

Definition Classes
Predictor
def setLabelCol(value: String): GBMRegressor

Definition Classes
Predictor
def setLearningRate(value: Double): GBMRegressor.this.type
def setLoss(value: String): GBMRegressor.this.type
def setMaxIter(value: Int): GBMRegressor.this.type
def setPredictionCol(value: String): GBMRegressor

Definition Classes
Predictor
def setTol(value: Double): GBMRegressor.this.type
def setWeightCol(value: String): GBMRegressor.this.type
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final val tol: DoubleParam

Definition Classes
HasTol
def train(dataset: Dataset[_]): GBMRegressionModel

Attributes
protected
Definition Classes
GBMRegressor → Predictor
def transformSchema(schema: StructType): StructType

Definition Classes
Predictor → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

Definition Classes
GBMRegressor → Identifiable
def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

Attributes
protected
Definition Classes
PredictorParams
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final val weightCol: Param[String]

Definition Classes
HasWeightCol
def write: MLWriter

Definition Classes
GBMRegressor → MLWritable

Related Docs: object GBMRegressor | package regression

class GBMRegressor extends Predictor[Vector, GBMRegressor, GBMRegressionModel] with GBMRegressorParams with MLWritable

Instance Constructors

new GBMRegressor()

new GBMRegressor(uid: String)

Value Members

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]

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

def clone(): AnyRef

def copy(extra: ParamMap): GBMRegressor

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

def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: Param[String]

def finalize(): Unit

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

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

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

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

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

def getLearningRate: Double

def getLoss: String

final def getMaxIter: Int

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

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

final def getPredictionCol: String

final def getSeed: Long

final def getTol: Double

final def getWeightCol: String

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

final val labelCol: Param[String]

val learningRate: Param[Double]

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 loss: Param[String]

final val maxIter: IntParam

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

final val predictionCol: Param[String]

def save(path: String): Unit

final val seed: LongParam

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

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