abstract class RankerModel[FeaturesType, M <: RankerModel[FeaturesType, M]] extends PredictionModel[FeaturesType, M] with PredictorParams
Model produced by a Ranker
.
- FeaturesType
Type of input features. E.g., org.apache.spark.mllib.linalg.Vector
- M
Concrete Model type.
Linear Supertypes
Known Subclasses
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- RankerModel
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
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Instance Constructors
- new RankerModel()
Abstract Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
$[T](param: Param[T]): T
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final
def
==(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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final
def
clear(param: Param[_]): RankerModel.this.type
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def
clone(): AnyRef
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
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final
def
eq(arg0: AnyRef): Boolean
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def
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def
explainParam(param: Param[_]): String
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def
explainParams(): String
- Definition Classes
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final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
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final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
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final
val
featuresCol: Param[String]
- Definition Classes
- HasFeaturesCol
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def
featuresDataType: DataType
- Attributes
- protected
- Definition Classes
- PredictionModel
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def
finalize(): Unit
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final
def
get[T](param: Param[T]): Option[T]
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def
getClass(): Class[_]
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final
def
getDefault[T](param: Param[T]): Option[T]
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final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
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final
def
getLabelCol: String
- Definition Classes
- HasLabelCol
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
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def
getParam(paramName: String): Param[Any]
- Definition Classes
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final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
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final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
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def
hasParam(paramName: String): Boolean
- Definition Classes
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def
hasParent: Boolean
- Definition Classes
- Model
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def
hashCode(): Int
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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def
isDefined(param: Param[_]): Boolean
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def
isSet(param: Param[_]): Boolean
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def
isTraceEnabled(): Boolean
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final
val
labelCol: Param[String]
- Definition Classes
- HasLabelCol
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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def
logInfo(msg: ⇒ String): Unit
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def
logName: String
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
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def
logTrace(msg: ⇒ String): Unit
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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def
logWarning(msg: ⇒ String): Unit
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final
def
ne(arg0: AnyRef): Boolean
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final
def
notify(): Unit
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final
def
notifyAll(): Unit
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def
numFeatures: Int
- Definition Classes
- PredictionModel
- Annotations
- @Since( "1.6.0" )
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[M]
- Definition Classes
- Model
-
final
val
predictionCol: Param[String]
- Definition Classes
- HasPredictionCol
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final
def
set(paramPair: ParamPair[_]): RankerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
set(param: String, value: Any): RankerModel.this.type
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- protected
- Definition Classes
- Params
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final
def
set[T](param: Param[T], value: T): RankerModel.this.type
- Definition Classes
- Params
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final
def
setDefault(paramPairs: ParamPair[_]*): RankerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
setDefault[T](param: Param[T], value: T): RankerModel.this.type
- Attributes
- protected[ml]
- Definition Classes
- Params
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def
setFeaturesCol(value: String): M
- Definition Classes
- PredictionModel
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def
setParent(parent: Estimator[M]): M
- Definition Classes
- Model
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def
setPredictionCol(value: String): M
- Definition Classes
- PredictionModel
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
- Definition Classes
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def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- PredictionModel → Transformer
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
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def
transformImpl(dataset: Dataset[_]): DataFrame
- Attributes
- protected
- Definition Classes
- PredictionModel
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def
transformSchema(schema: StructType): StructType
- Definition Classes
- PredictionModel → PipelineStage
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def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
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def
validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
- Attributes
- protected
- Definition Classes
- PredictorParams
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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