XGBoostRegressionModel

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

var _booster: Booster

final def asInstanceOf[T0]: T0

def booster: Booster

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

def clone(): AnyRef

def copy(extra: ParamMap): XGBoostRegressionModel

def copyValues[T <: class="extype" name="org.apache.spark.ml.param.Params">Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: class="extype" name="org.apache.spark.ml.param.Params">Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def eval(evalDataset: RDD[org.apache.spark.ml.feature.LabeledPoint], evalName: String, evalFunc: EvalTrait = null, iter: Int = 1, useExternalCache: Boolean = false): String

evaluate XGBoostModel with a RDD-wrapped dataset

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

final val featuresCol: Param[String]

def featuresDataType: DataType

def finalize(): Unit

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

def hasParam(paramName: String): Boolean

def hasParent: Boolean

def hashCode(): Int

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

def numFeatures: Int

lazy val params: Array[Param[_]]

var parent: Estimator[XGBoostModel]

def predict(features: Vector): Double

def predict(testSet: RDD[Vector], useExternalCache: Boolean = false): RDD[Array[Array[Float]]]

Predict result with the given test set (represented as RDD)

def predict(testSet: RDD[DenseVector], missingValue: Float): RDD[Array[Array[Float]]]

Predict result with the given test set (represented as RDD)

def predictLeaves(testSet: RDD[Vector]): RDD[Array[Array[Float]]]

Predict leaf instances with the given test set (represented as RDD)

final val predictionCol: Param[String]

def produceRowRDD(testSet: Dataset[_], outputMargin: Boolean = false, predLeaf: Boolean = false): RDD[Row]

def saveModelAsHadoopFile(modelPath: String)(implicit sc: SparkContext): Unit

Save the model as to HDFS-compatible file system.

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

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

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

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

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

def setExternalMemory(value: Boolean): XGBoostModel

def setFeaturesCol(value: String): XGBoostModel

def setLabelCol(name: String): XGBoostModel

def setParent(parent: Estimator[XGBoostModel]): XGBoostModel

def setPredictionCol(value: String): XGBoostModel

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

def toString(): String

def transform(testSet: Dataset[_]): DataFrame

produces the prediction results and append as an additional column in the original dataset NOTE: the prediction results is kept as the original format of xgboost

def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

def transformImpl(testSet: Dataset[_]): DataFrame

def transformLeaf(testSet: Dataset[_]): DataFrame

append leaf index of each row as an additional column in the original dataset

def transformSchema(schema: StructType): StructType

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

val uid: String

final val useExternalMemory: Param[Boolean]

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

def validateParams(): Unit