BoostingClassifier

Instance Constructors

new BoostingClassifier()
new BoostingClassifier(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
def avgLoss(lossColName: String, boostProbaColName: String)(df: DataFrame): Double

Definition Classes
BoostingParams
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
def beta(avgl: Double, numClasses: Int = 2): Double

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

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
BoostingClassifier → 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 evaluateOnValidation(numClasses: Int, weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

Definition Classes
BoostingParams
def evaluateOnValidation(weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

Definition Classes
BoostingParams
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
def extractBoostedBag(poissonProbaColName: String, seed: Long)(df: DataFrame): DataFrame

Definition Classes
BoostingParams
def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

Attributes
protected
Definition Classes
Classifier
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[_]): BoostingClassificationModel

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

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

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

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
def fitBaseLearner(baseLearner: EnsemblePredictorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType

Definition Classes
HasBaseLearner
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 getLoss: String

Definition Classes
BoostingClassifierParams
def getNumBaseLearners: Int

Definition Classes
HasNumBaseLearners
def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

Attributes
protected
Definition Classes
Classifier
def getNumRound: Int

Definition Classes
HasNumRound
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 getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
final def getSeed: Long

Definition Classes
HasSeed
final def getTol: Double

Definition Classes
HasTol
final def getValidationIndicatorCol: String

Definition Classes
HasValidationIndicatorCol
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
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: "exponential" (default = exponential)

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numBaseLearners: Param[Int]

param for the number of base learners of the algorithm
param for the number of base learners of the algorithm

Definition Classes
HasNumBaseLearners
val numRound: Param[Int]

param for the number of round waiting for next decrease in validation set
param for the number of round waiting for next decrease in validation set

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

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
def probabilize(boostWeightColName: String, boostProbaColName: String, poissonProbaColName: String)(df: DataFrame): DataFrame

Definition Classes
BoostingParams
final val rawPredictionCol: Param[String]

Definition Classes
HasRawPredictionCol
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[_]): BoostingClassifier.this.type

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

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

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

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

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

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

Definition Classes
Predictor
def setLoss(value: String): BoostingClassifier.this.type
def setNumBaseLearners(value: Int): BoostingClassifier.this.type
def setNumRound(value: Int): BoostingClassifier.this.type
def setPredictionCol(value: String): BoostingClassifier

Definition Classes
Predictor
def setRawPredictionCol(value: String): BoostingClassifier

Definition Classes
Classifier
def setSeed(value: Long): BoostingClassifier.this.type
def setTol(value: Double): BoostingClassifier.this.type
def setValidationIndicatorCol(value: String): BoostingClassifier.this.type
def setWeightCol(value: String): BoostingClassifier.this.type
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def terminate(avgl: Double, withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation, numClasses: Double = 2.0): (Int, Double, Int)

Definition Classes
BoostingParams
def terminateVal(withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation): (Int, Double, Int)

Definition Classes
BoostingParams
def toString(): String

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

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

Attributes
protected
Definition Classes
BoostingClassifier → 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
BoostingClassifier → Identifiable
def updateWeights(boostWeightColName: String, lossColName: String, beta: Double, updatedBoostWeightColName: String)(df: DataFrame): DataFrame

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

Attributes
protected
Definition Classes
ClassifierParams → PredictorParams
def validateLabel(label: Double, numClasses: Int): Unit

Validates the label on the classifier is a valid integer in the range [0, numClasses).
Validates the label on the classifier is a valid integer in the range [0, numClasses).
label
The label to validate.
numClasses
Number of classes label can take. Labels must be integers in the range [0, numClasses).

Attributes
protected
def validateNumClasses(numClasses: Int): Unit

Validates that number of classes is greater than zero.
Validates that number of classes is greater than zero.
numClasses
Number of classes label can take.

Attributes
protected
final val validationIndicatorCol: Param[String]

Definition Classes
HasValidationIndicatorCol
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( ... )
def weight(beta: Double): Double

Definition Classes
BoostingParams
final val weightCol: Param[String]

Definition Classes
HasWeightCol
def write: MLWriter

Definition Classes
BoostingClassifier → MLWritable

Inherited from MLWritable

Inherited from BoostingClassifierParams

Inherited from BoostingParams

Inherited from HasNumRound

Inherited from HasTol

Inherited from HasValidationIndicatorCol

Inherited from HasBaseLearner

Inherited from HasSeed

Inherited from HasWeightCol

Inherited from HasNumBaseLearners

Inherited from Classifier[Vector, BoostingClassifier, BoostingClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, BoostingClassifier, BoostingClassificationModel]

Related Docs: object BoostingClassifier | package classification

class BoostingClassifier extends Classifier[Vector, BoostingClassifier, BoostingClassificationModel] with BoostingClassifierParams with MLWritable

Instance Constructors

new BoostingClassifier()

new BoostingClassifier(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

def avgLoss(lossColName: String, boostProbaColName: String)(df: DataFrame): Double

val baseLearner: Param[EnsemblePredictorType]

def beta(avgl: Double, numClasses: Int = 2): Double

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

def clone(): AnyRef

def copy(extra: ParamMap): BoostingClassifier

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 evaluateOnValidation(numClasses: Int, weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

def evaluateOnValidation(weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

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

def explainParams(): String

def extractBoostedBag(poissonProbaColName: String, seed: Long)(df: DataFrame): DataFrame

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

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[_]): BoostingClassificationModel

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

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

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

def fitBaseLearner(baseLearner: EnsemblePredictorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType

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 getLoss: String

def getNumBaseLearners: Int

def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

def getNumRound: Int

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

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

final def getPredictionCol: String

final def getRawPredictionCol: String

final def getSeed: Long

final def getTol: Double

final def getValidationIndicatorCol: String

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]

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 def ne(arg0: AnyRef): Boolean