CatBoostPredictorTrait

abstract def copy(extra: ParamMap): Learner

abstract def createModel(fullModel: TFullModel): Model

abstract val uid: String

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

def addEstimatedCtrFeatures(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool], updatedCatBoostJsonParams: JObject, classTargetPreprocessor: Option[TClassTargetPreprocessor] = None, serializedLabelConverter: TVector_i8 = new TVector_i8): (Pool, Array[Pool], CtrsContext)

final def asInstanceOf[T0]: T0

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

def clone(): AnyRef

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

def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: Param[String]

def finalize(): Unit

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.

def fit(dataset: Dataset[_]): Model

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

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

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

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

final def getFeaturesCol: String

final def getLabelCol: String

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

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

final def getPredictionCol: String

final def getWeightCol: String

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

def hasParam(paramName: String): 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

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

final val predictionCol: Param[String]

def preprocessBeforeTraining(quantizedTrainPool: Pool, quantizedEvalPools: Array[Pool]): (Pool, Array[Pool], CatBoostTrainingContext)

override in descendants if necessary

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): CatBoostPredictorTrait.this

final def set(param: String, value: Any): CatBoostPredictorTrait.this

final def set[T](param: Param[T], value: T): CatBoostPredictorTrait.this

final def setDefault(paramPairs: ParamPair[_]*): CatBoostPredictorTrait.this

final def setDefault[T](param: Param[T], value: T): CatBoostPredictorTrait.this

def setFeaturesCol(value: String): Learner

def setLabelCol(value: String): Learner

def setPredictionCol(value: String): Learner

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def train(dataset: Dataset[_]): Model

def transformSchema(schema: StructType): StructType

def transformSchema(schema: StructType, logging: Boolean): StructType

def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

final val weightCol: Param[String]

def write: MLWriter