c
org.apache.spark.ml.classification
BoostingClassificationModel
Companion object BoostingClassificationModel
class BoostingClassificationModel extends ProbabilisticClassificationModel[Vector, BoostingClassificationModel] with BoostingClassifierParams with MLWritable
- Source
- BoostingClassifier.scala
Linear Supertypes
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Inherited
- BoostingClassificationModel
- MLWritable
- BoostingClassifierParams
- BoostingParams
- HasAggregationDepth
- HasCheckpointInterval
- HasBaseLearner
- HasWeightCol
- HasNumBaseLearners
- ProbabilisticClassificationModel
- ProbabilisticClassifierParams
- HasThresholds
- HasProbabilityCol
- ClassificationModel
- ClassifierParams
- HasRawPredictionCol
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
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Visibility
- Public
- Protected
Instance Constructors
- new BoostingClassificationModel(numClasses: Int, weights: Array[Double], models: Array[EnsemblePredictionModelType])
- new BoostingClassificationModel(uid: String, numClasses: Int, weights: Array[Double], 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 val aggregationDepth: IntParam
- Definition Classes
- HasAggregationDepth
- val algorithm: Param[String]
Discrete (SAMME) or Real (SAMME.R) boosting algorithm.
Discrete (SAMME) or Real (SAMME.R) boosting algorithm. (case-insensitive) Supported: "discrete", "real". (default = median)
- Definition Classes
- BoostingClassifierParams
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- val baseLearner: Param[EnsembleClassifierType]
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 val checkpointInterval: IntParam
- Definition Classes
- HasCheckpointInterval
- final def clear(param: Param[_]): BoostingClassificationModel.this.type
- Definition Classes
- Params
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- def copy(extra: ParamMap): BoostingClassificationModel
- Definition Classes
- BoostingClassificationModel → 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: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def explainParam(param: Param[_]): String
- Definition Classes
- Params
- def explainParams(): String
- Definition Classes
- Params
- def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]
- Attributes
- protected
- Definition Classes
- ClassifierParams
- def extractInstances(dataset: Dataset[_], validateInstance: (Instance) => Unit): RDD[Instance]
- Attributes
- protected
- Definition Classes
- PredictorParams
- def extractInstances(dataset: Dataset[_]): RDD[Instance]
- Attributes
- protected
- Definition Classes
- PredictorParams
- 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 featuresDataType: DataType
- Attributes
- protected
- Definition Classes
- PredictionModel
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable])
- def fitBaseLearner(baseLearner: EnsembleClassifierType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
- Attributes
- protected
- Definition Classes
- HasBaseLearner
- final def get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
- final def getAggregationDepth: Int
- Definition Classes
- HasAggregationDepth
- def getAlgorithm: String
- Definition Classes
- BoostingClassifierParams
- def getBaseLearner: EnsembleClassifierType
- Definition Classes
- HasBaseLearner
- final def getCheckpointInterval: Int
- Definition Classes
- HasCheckpointInterval
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
- 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
- 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 getProbabilityCol: String
- Definition Classes
- HasProbabilityCol
- final def getRawPredictionCol: String
- Definition Classes
- HasRawPredictionCol
- def getThresholds: Array[Double]
- Definition Classes
- HasThresholds
- 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
- Annotations
- @native()
- 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 models: Array[EnsemblePredictionModelType]
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- 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 numClasses: Int
- Definition Classes
- BoostingClassificationModel → ClassificationModel
- def numFeatures: Int
- Definition Classes
- PredictionModel
- Annotations
- @Since("1.6.0")
- val numModels: Int
- lazy val params: Array[Param[_]]
- Definition Classes
- Params
- var parent: Estimator[BoostingClassificationModel]
- Definition Classes
- Model
- def predict(features: Vector): Double
- Definition Classes
- ClassificationModel → PredictionModel
- def predictProbability(features: Vector): Vector
- Definition Classes
- ProbabilisticClassificationModel
- Annotations
- @Since("3.0.0")
- def predictRaw(features: Vector): Vector
- Definition Classes
- BoostingClassificationModel → ClassificationModel
- final val predictionCol: Param[String]
- Definition Classes
- HasPredictionCol
- def probability2prediction(probability: Vector): Double
- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel
- final val probabilityCol: Param[String]
- Definition Classes
- HasProbabilityCol
- def raw2prediction(rawPrediction: Vector): Double
- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel
- def raw2probability(rawPrediction: Vector): Vector
- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel
- def raw2probabilityInPlace(rawPrediction: Vector): Vector
- Attributes
- protected
- Definition Classes
- BoostingClassificationModel → ProbabilisticClassificationModel
- final val rawPredictionCol: Param[String]
- Definition Classes
- HasRawPredictionCol
- def save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
- final def set(paramPair: ParamPair[_]): BoostingClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def set(param: String, value: Any): BoostingClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def set[T](param: Param[T], value: T): BoostingClassificationModel.this.type
- Definition Classes
- Params
- final def setDefault(paramPairs: ParamPair[_]*): BoostingClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def setDefault[T](param: Param[T], value: T): BoostingClassificationModel.this.type
- Attributes
- protected
- Definition Classes
- Params
- def setFeaturesCol(value: String): BoostingClassificationModel
- Definition Classes
- PredictionModel
- def setParent(parent: Estimator[BoostingClassificationModel]): BoostingClassificationModel
- Definition Classes
- Model
- def setPredictionCol(value: String): BoostingClassificationModel
- Definition Classes
- PredictionModel
- def setProbabilityCol(value: String): BoostingClassificationModel
- Definition Classes
- ProbabilisticClassificationModel
- def setRawPredictionCol(value: String): BoostingClassificationModel
- Definition Classes
- ClassificationModel
- def setThresholds(value: Array[Double]): BoostingClassificationModel
- Definition Classes
- ProbabilisticClassificationModel
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- val thresholds: DoubleArrayParam
- Definition Classes
- HasThresholds
- def toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- def transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel → 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()
- final def transformImpl(dataset: Dataset[_]): DataFrame
- Definition Classes
- ClassificationModel → PredictionModel
- def transformSchema(schema: StructType): StructType
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
- def transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
- val uid: String
- Definition Classes
- BoostingClassificationModel → Identifiable
- def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
- Attributes
- protected
- Definition Classes
- ProbabilisticClassifierParams → ClassifierParams → PredictorParams
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final val weightCol: Param[String]
- Definition Classes
- HasWeightCol
- val weights: Array[Double]
- def write: MLWriter
- Definition Classes
- BoostingClassificationModel → MLWritable