BaggingClassificationModel

Instance Constructors

new BaggingClassificationModel(subSpaces: Array[Array[Int]], models: Array[EnsemblePredictionModelType])
new BaggingClassificationModel(uid: String, subspaces: Array[SubSpace], models: Array[EnsemblePredictionModelType])

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[_]): BaggingClassificationModel.this.type

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
BaggingClassificationModel → Model → Transformer → 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
final def extractParamMap(): ParamMap

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

Definition Classes
Params
def extractSubBag(bagColName: String, index: Int, featuresColName: String, subspace: SubSpace)(df: DataFrame): DataFrame

Definition Classes
HasSubBag
final val featuresCol: Param[String]

Definition Classes
HasFeaturesCol
def featuresDataType: DataType

Attributes
protected
Definition Classes
PredictionModel
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
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 getNumBaseLearners: Int

Definition Classes
HasNumBaseLearners
def getNumFeatures(dataset: DataFrame, featuresCol: String): Int

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

Definition Classes
Params
def getParallelism: Int

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

Definition Classes
Params
final def getPredictionCol: String

Definition Classes
HasPredictionCol
final def getRawPredictionCol: String

Definition Classes
HasRawPredictionCol
def getReplacement: Boolean

Definition Classes
HasSubBag
def getSampleRatio: Double

Definition Classes
HasSubBag
final def getSeed: Long

Definition Classes
HasSeed
def getSubspaceRatio: Double

Definition Classes
HasSubBag
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 hasParent: Boolean

Definition Classes
Model
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
def mkSubspace(sampleRatio: Double, numFeatures: Int, seed: Long): SubSpace

Definition Classes
HasSubBag
val models: Array[EnsemblePredictionModelType]
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 numBaseModels: Int
def numFeatures: Int

Definition Classes
PredictionModel
Annotations
@Since( "1.6.0" )
val parallelism: IntParam

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

Definition Classes
Params
var parent: Estimator[BaggingClassificationModel]

Definition Classes
Model
def predict(features: Vector): Double

Definition Classes
BaggingClassificationModel → PredictionModel
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
final val rawPredictionCol: Param[String]

Definition Classes
HasRawPredictionCol
val replacement: Param[Boolean]

param for whether samples are drawn with replacement
param for whether samples are drawn with replacement

Definition Classes
HasSubBag
val sampleRatio: Param[Double]

param for ratio of rows sampled out of the dataset
param for ratio of rows sampled out of the dataset

Definition Classes
HasSubBag
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[_]): BaggingClassificationModel.this.type

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

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

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): BaggingClassificationModel.this.type

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

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

Definition Classes
PredictionModel
def setParent(parent: Estimator[BaggingClassificationModel]): BaggingClassificationModel

Definition Classes
Model
def setPredictionCol(value: String): BaggingClassificationModel

Definition Classes
PredictionModel
def slicer(subspace: SubSpace): (Vector) ⇒ Vector

Definition Classes
HasSubBag
val subspaceRatio: Param[Double]

param for ratio of rows sampled out of the dataset
param for ratio of rows sampled out of the dataset

Definition Classes
HasSubBag
val subspaces: Array[SubSpace]
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataset: Dataset[_]): DataFrame

Definition Classes
PredictionModel → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformImpl(dataset: Dataset[_]): DataFrame

Attributes
protected
Definition Classes
PredictionModel
def transformSchema(schema: StructType): StructType

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

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

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

Attributes
protected
Definition Classes
ClassifierParams → 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 withBag(withReplacement: Boolean, sampleRatio: Double, numberSamples: Int, seed: Long, bagColName: String)(df: DataFrame): DataFrame

Definition Classes
HasSubBag
def write: MLWriter

Definition Classes
BaggingClassificationModel → MLWritable

Related Docs: object BaggingClassificationModel | package classification

class BaggingClassificationModel extends PredictionModel[Vector, BaggingClassificationModel] with BaggingClassifierParams with MLWritable

Instance Constructors

new BaggingClassificationModel(subSpaces: Array[Array[Int]], models: Array[EnsemblePredictionModelType])

new BaggingClassificationModel(uid: String, subspaces: Array[SubSpace], models: Array[EnsemblePredictionModelType])

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[_]): BaggingClassificationModel.this.type

def clone(): AnyRef

def copy(extra: ParamMap): BaggingClassificationModel

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

def extractSubBag(bagColName: String, index: Int, featuresColName: String, subspace: SubSpace)(df: DataFrame): DataFrame

final val featuresCol: Param[String]

def featuresDataType: DataType

def finalize(): Unit

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 getNumBaseLearners: Int

def getNumFeatures(dataset: DataFrame, featuresCol: String): Int

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

def getParallelism: Int

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

final def getPredictionCol: String

final def getRawPredictionCol: String

def getReplacement: Boolean

def getSampleRatio: Double

final def getSeed: Long

def getSubspaceRatio: Double

final def getWeightCol: String

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

def hasParam(paramName: String): Boolean

def hasParent: 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

def mkSubspace(sampleRatio: Double, numFeatures: Int, seed: Long): SubSpace

val models: Array[EnsemblePredictionModelType]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numBaseLearners: Param[Int]

val numBaseModels: Int

def numFeatures: Int

val parallelism: IntParam

lazy val params: Array[Param[_]]