trait CatBoostPredictorTrait[Learner <: Predictor[Vector, Learner, Model], Model <: PredictionModel[Vector, Model]] extends Predictor[Vector, Learner, Model] with DatasetParamsTrait with DefaultParamsWritable
Base trait with common functionality for both CatBoostClassifier and CatBoostRegressor
- Self Type
- CatBoostPredictorTrait[Learner, Model] with TrainingParamsTrait
- Alphabetic
- By Inheritance
- CatBoostPredictorTrait
- DefaultParamsWritable
- MLWritable
- DatasetParamsTrait
- HasWeightCol
- Predictor
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Estimator
- PipelineStage
- Logging
- Params
- Serializable
- Serializable
- Identifiable
- AnyRef
- Any
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Abstract Value Members
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abstract
def
copy(extra: ParamMap): Learner
- Definition Classes
- Predictor → Estimator → PipelineStage → Params
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abstract
def
createModel(fullModel: TFullModel): Model
- Attributes
- protected
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abstract
val
uid: String
- Definition Classes
- Identifiable
Concrete Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
##(): Int
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final
def
$[T](param: Param[T]): T
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- protected
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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def
addEstimatedCtrFeatures(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool], updatedCatBoostJsonParams: JObject, classTargetPreprocessor: Option[TClassTargetPreprocessor] = None, serializedLabelConverter: TVector_i8 = new TVector_i8): (Pool, Array[Pool], CtrsContext)
- returns
(preprocessedTrainPool, preprocessedEvalPools, ctrsContext)
- Attributes
- protected
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
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final
def
clear(param: Param[_]): CatBoostPredictorTrait.this
- Definition Classes
- Params
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def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
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- protected
- Definition Classes
- Params
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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def
explainParam(param: Param[_]): String
- Definition Classes
- Params
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def
explainParams(): String
- Definition Classes
- Params
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def
extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
- Attributes
- protected
- Definition Classes
- PredictorParams
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def
extractInstances(dataset: Dataset[_]): RDD[Instance]
- Attributes
- protected
- Definition Classes
- PredictorParams
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def
extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
- Attributes
- protected
- Definition Classes
- Predictor
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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
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
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- @throws( classOf[java.lang.Throwable] )
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def
fit(trainPool: Pool, evalPools: Array[Pool] = Array[Pool]()): Model
Additional variant of
fit
method that accepts CatBoost's Pool s and allows to specify additional datasets for computing evaluation metrics and overfitting detection similarily to CatBoost's other APIs.Additional variant of
fit
method that accepts CatBoost's Pool s and allows to specify additional datasets for computing evaluation metrics and overfitting detection similarily to CatBoost's other APIs.- trainPool
The input training dataset.
- evalPools
The validation datasets used for the following processes:
- overfitting detector
- best iteration selection
- monitoring metrics' changes
- returns
trained model
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def
fit(dataset: Dataset[_]): Model
- Definition Classes
- Predictor → Estimator
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def
fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[Model]
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
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def
fit(dataset: Dataset[_], paramMap: ParamMap): Model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" )
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def
fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): Model
- Definition Classes
- Estimator
- Annotations
- @Since( "2.0.0" ) @varargs()
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final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
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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
- Params
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def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
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final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
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final
def
getWeightCol: String
- Definition Classes
- HasWeightCol
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final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
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def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
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def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
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final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
- Params
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
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def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
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final
val
labelCol: Param[String]
- Definition Classes
- HasLabelCol
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def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logDebug(msg: ⇒ String): Unit
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- Definition Classes
- Logging
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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- Definition Classes
- Logging
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def
logError(msg: ⇒ String): Unit
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- Logging
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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- Definition Classes
- Logging
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def
logInfo(msg: ⇒ String): Unit
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- Definition Classes
- Logging
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def
logName: String
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- protected
- Definition Classes
- Logging
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def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
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- protected
- Definition Classes
- Logging
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def
logTrace(msg: ⇒ String): Unit
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- protected
- Definition Classes
- Logging
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
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- protected
- Definition Classes
- Logging
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def
logWarning(msg: ⇒ String): Unit
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- protected
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- Logging
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
- Definition Classes
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- Annotations
- @native()
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
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final
val
predictionCol: Param[String]
- Definition Classes
- HasPredictionCol
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def
preprocessBeforeTraining(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool]): (Pool, Array[Pool], CatBoostTrainingContext)
override in descendants if necessary
override in descendants if necessary
- returns
(preprocessedTrainPool, preprocessedEvalPools, catBoostTrainingContext)
- Attributes
- protected
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def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
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final
def
set(paramPair: ParamPair[_]): CatBoostPredictorTrait.this
- Attributes
- protected
- Definition Classes
- Params
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final
def
set(param: String, value: Any): CatBoostPredictorTrait.this
- Attributes
- protected
- Definition Classes
- Params
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final
def
set[T](param: Param[T], value: T): CatBoostPredictorTrait.this
- Definition Classes
- Params
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final
def
setDefault(paramPairs: ParamPair[_]*): CatBoostPredictorTrait.this
- Attributes
- protected
- Definition Classes
- Params
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final
def
setDefault[T](param: Param[T], value: T): CatBoostPredictorTrait.this
- Attributes
- protected
- Definition Classes
- Params
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def
setFeaturesCol(value: String): Learner
- Definition Classes
- Predictor
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def
setLabelCol(value: String): Learner
- Definition Classes
- Predictor
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def
setPredictionCol(value: String): Learner
- Definition Classes
- Predictor
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
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def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
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def
train(dataset: Dataset[_]): Model
- Attributes
- protected
- Definition Classes
- CatBoostPredictorTrait → Predictor
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def
transformSchema(schema: StructType): StructType
- Definition Classes
- Predictor → 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
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
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final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
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final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
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final
val
weightCol: Param[String]
- Definition Classes
- HasWeightCol
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def
write: MLWriter
- Definition Classes
- DefaultParamsWritable → MLWritable