OpRandomForestClassifier

Instance Constructors

new OpRandomForestClassifier(uid: String = UID[OpRandomForestClassifier])

uid
stage uid

Type Members

final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

Definition Classes
OpPipelineStage2 → OpPipelineStage → InputParams
final type OutputFeatures = FeatureLike[Prediction]

Definition Classes
OpPipelineStage → OpPipelineStageBase

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
final val cacheNodeIds: BooleanParam

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

Definition Classes
OpPipelineStage2 → InputParams
def checkSerializable: Try[Unit]

Definition Classes
OpPipelineStageBase
final val checkpointInterval: IntParam

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

Definition Classes
Params
def clone(): AnyRef

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

Definition Classes
OpPipelineStageBase → 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
final val featureSubsetStrategy: Param[String]

Definition Classes
TreeEnsembleParams
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[_]): OpPredictorWrapperModel[RandomForestClassificationModel]

Function that fits the binary model
Function that fits the binary model

Definition Classes
OpPredictorWrapper → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[RandomForestClassificationModel]]

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

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

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

Definition Classes
Params
final def getCacheNodeIds: Boolean

Definition Classes
DecisionTreeParams
final def getCheckpointInterval: Int

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

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

Definition Classes
Params
final def getFeatureSubsetStrategy: String

Definition Classes
TreeEnsembleParams
final def getFeaturesCol: String

Definition Classes
HasFeaturesCol
final def getImpurity: String

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

Definition Classes
InputParams
final def getInputFeatures(): Array[OPFeature]

Definition Classes
InputParams
final def getInputSchema(): StructType

Definition Classes
OpPipelineStageParams
final def getLabelCol: String

Definition Classes
HasLabelCol
final def getMaxBins: Int

Definition Classes
DecisionTreeParams
final def getMaxDepth: Int

Definition Classes
DecisionTreeParams
final def getMaxMemoryInMB: Int

Definition Classes
DecisionTreeParams
final def getMetadata(): Metadata

Definition Classes
OpPipelineStageParams
final def getMinInfoGain: Double

Definition Classes
DecisionTreeParams
final def getMinInstancesPerNode: Int

Definition Classes
DecisionTreeParams
final def getNumTrees: Int

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

Definition Classes
Params
def getOutput(): FeatureLike[Prediction]

Definition Classes
OpPipelineStage2 → OpPipelineStageBase
final def getOutputFeatureName: String

Definition Classes
OpPipelineStage
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
final def getSeed: Long

Definition Classes
HasSeed
def getSparkMlStage(): Option[RandomForestClassifier]

Definition Classes
SparkWrapperParams
def getStageSavePath(): Option[String]

Definition Classes
SparkWrapperParams
final def getSubsamplingRate: Double

Definition Classes
TreeEnsembleParams
def getThresholds: Array[Double]

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

Definition Classes
InputParams
final def getTransientFeatures(): Array[TransientFeature]

Definition Classes
InputParams
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
final val impurity: Param[String]

Definition Classes
TreeClassifierParams
final def in1: TransientFeature

Attributes
protected
Definition Classes
HasIn1
final def in2: TransientFeature

Attributes
protected
Definition Classes
HasIn2
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

Attributes
protected
Definition Classes
Logging
final def inputAsArray(in: InputFeatures): Array[OPFeature]

Definition Classes
OpPipelineStage2 → InputParams
val inputParam1Name: String

Definition Classes
OpPredictorWrapper
val inputParam2Name: String

Definition Classes
OpPredictorWrapper
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
final val maxBins: IntParam

Definition Classes
DecisionTreeParams
final val maxDepth: IntParam

Definition Classes
DecisionTreeParams
final val maxMemoryInMB: IntParam

Definition Classes
DecisionTreeParams
final val minInfoGain: DoubleParam

Definition Classes
DecisionTreeParams
final val minInstancesPerNode: IntParam

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

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final val numTrees: IntParam

Definition Classes
RandomForestParams
def onGetMetadata(): Unit

Attributes
protected
Definition Classes
OpPipelineStageParams
def onSetInput(): Unit

Attributes
protected
Definition Classes
OpRandomForestClassifier → OpPipelineStageBase
val operationName: String

Definition Classes
OpPredictorWrapper → OpPipelineStageBase
final def outputAsArray(out: OutputFeatures): Array[OPFeature]

Definition Classes
OpPipelineStage → OpPipelineStageBase
def outputFeatureUid: String

Attributes
protected[com.salesforce.op]
Definition Classes
OpPipelineStage2 → OpPipelineStage
def outputIsResponse: Boolean

Definition Classes
OpPipelineStage
val outputParamName: String

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

Definition Classes
Params
final val predictionCol: Param[String]

Definition Classes
HasPredictionCol
val predictor: RandomForestClassifier

the predictor to wrap
the predictor to wrap

Definition Classes
OpPredictorWrapper
final val probabilityCol: Param[String]

Definition Classes
HasProbabilityCol
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[_]): OpRandomForestClassifier.this.type

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

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

Definition Classes
Params
def setCacheNodeIds(value: Boolean): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
def setCheckpointInterval(value: Int): OpRandomForestClassifier.this.type

Specifies how often to checkpoint the cached node IDs.
Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in org.apache.spark.SparkContext. Must be at least 1. (default = 10)

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
final def setDefault(paramPairs: ParamPair[_]*): OpRandomForestClassifier.this.type

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

Attributes
protected
Definition Classes
Params
def setFeatureSubsetStrategy(value: String): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → TreeEnsembleParams
def setImpurity(value: String): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → TreeClassifierParams
final def setInput(features: InputFeatures): OpRandomForestClassifier.this.type

Definition Classes
OpPipelineStageBase
final def setInputFeatures[S <: OPFeature](features: Array[S]): OpRandomForestClassifier.this.type

Attributes
protected
Definition Classes
InputParams
def setMaxBins(value: Int): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
def setMaxDepth(value: Int): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
def setMaxMemoryInMB(value: Int): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
final def setMetadata(m: Metadata): OpRandomForestClassifier.this.type

Definition Classes
OpPipelineStageParams
def setMinInfoGain(value: Double): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
def setMinInstancesPerNode(value: Int): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
def setNumTrees(value: Int): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → RandomForestParams
def setOutputFeatureName(name: String): OpRandomForestClassifier.this.type

Definition Classes
OpPipelineStage
def setSeed(value: Long): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → DecisionTreeParams
def setSparkMlStage(stage: Option[RandomForestClassifier]): OpRandomForestClassifier.this.type

Attributes
protected
Definition Classes
SparkWrapperParams
def setStageSavePath(path: String): OpRandomForestClassifier.this.type

Definition Classes
SparkWrapperParams
def setSubsamplingRate(value: Double): OpRandomForestClassifier.this.type

Definition Classes
OpRandomForestClassifier → TreeEnsembleParams
def setThresholds(value: Array[Double]): OpRandomForestClassifier.this.type

Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
final val sparkInputColParamNames: StringArrayParam

Definition Classes
SparkWrapperParams
final val sparkMlStage: SparkStageParam[RandomForestClassifier]

Definition Classes
SparkWrapperParams
final val sparkOutputColParamNames: StringArrayParam

Definition Classes
SparkWrapperParams
final def stageName: String

Definition Classes
OpPipelineStageBase
final val subsamplingRate: DoubleParam

Definition Classes
TreeEnsembleParams
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
final val thresholds: DoubleArrayParam

Definition Classes
HasThresholds
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
final def transformSchema(schema: StructType): StructType

Definition Classes
OpPipelineStageBase
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

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

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

Definition Classes
OpPredictorWrapper → OpPipelineStage2
implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

Definition Classes
OpPredictorWrapper → OpPipelineStage2
val uid: String

stage uid
stage uid

Definition Classes
OpPredictorWrapper → 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( ... )
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 def write: MLWriter

Definition Classes
OpPipelineStageBase → MLWritable

Inherited from OpRandomForestClassifierParams

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from RandomForestClassifierParams

Inherited from TreeClassifierParams

Inherited from RandomForestParams

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[RandomForestClassifier, RandomForestClassificationModel]

Inherited from SparkWrapperParams[RandomForestClassifier]

Inherited from OpPipelineStage2[RealNN, OPVector, Prediction]

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[Prediction]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from Estimator[OpPredictorWrapperModel[RandomForestClassificationModel]]

Related Doc: package classification

class OpRandomForestClassifier extends OpPredictorWrapper[RandomForestClassifier, RandomForestClassificationModel] with OpRandomForestClassifierParams

Instance Constructors

new OpRandomForestClassifier(uid: String = UID[OpRandomForestClassifier])

Type Members

final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

final type OutputFeatures = FeatureLike[Prediction]

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

final val cacheNodeIds: BooleanParam

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

def checkSerializable: Try[Unit]

final val checkpointInterval: IntParam

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

def clone(): AnyRef

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

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[RandomForestClassificationModel]

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

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

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

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

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

final def getInputFeatures(): Array[OPFeature]

final def getInputSchema(): StructType

final def getLabelCol: String

final def getMaxBins: Int

final def getMaxDepth: Int

final def getMaxMemoryInMB: Int

final def getMetadata(): Metadata

final def getMinInfoGain: Double

final def getMinInstancesPerNode: Int

final def getNumTrees: Int

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

def getOutput(): FeatureLike[Prediction]

final def getOutputFeatureName: String

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

final def getPredictionCol: String

final def getProbabilityCol: String

final def getRawPredictionCol: String

final def getSeed: Long

def getSparkMlStage(): Option[RandomForestClassifier]

def getStageSavePath(): Option[String]

final def getSubsamplingRate: Double

def getThresholds: Array[Double]

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

final def getTransientFeatures(): Array[TransientFeature]

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]

val inputParam1Name: String

val inputParam2Name: String