XGBoost

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1. final def !=(arg0: Any): Boolean

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2. final def ##(): Int

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3. final def ==(arg0: Any): Boolean

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4. final def asInstanceOf[T0]: T0

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5. def clone(): AnyRef

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6. final def eq(arg0: AnyRef): Boolean

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7. def equals(arg0: Any): Boolean

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8. def finalize(): Unit

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9. final def getClass(): Class[_]

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10. def hashCode(): Int

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11. final def isInstanceOf[T0]: Boolean

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12. def loadModelFromHadoopFile(modelPath: String)(implicit sparkContext: SparkContext): XGBoostModel

    Load XGBoost model from path in HDFS-compatible file system

    Load XGBoost model from path in HDFS-compatible file system

    modelPath

    The path of the file representing the model

    returns

    The loaded model

13. final def ne(arg0: AnyRef): Boolean

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14. final def notify(): Unit

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15. final def notifyAll(): Unit

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

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17. def toString(): String

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18. def train(trainingData: RDD[org.apache.spark.ml.feature.LabeledPoint], params: Map[String, Any], round: Int, nWorkers: Int, obj: ObjectiveTrait = null, eval: EvalTrait = null, useExternalMemory: Boolean = false, missing: Float = Float.NaN): XGBoostModel

    train XGBoost model with the RDD-represented data

    train XGBoost model with the RDD-represented data

    trainingData

    the trainingset represented as RDD

    params

    Map containing the configuration entries

    round

    the number of iterations

    nWorkers

    the number of xgboost workers, 0 by default which means that the number of workers equals to the partition number of trainingData RDD

    obj

    the user-defined objective function, null by default

    eval

    the user-defined evaluation function, null by default

    useExternalMemory

    indicate whether to use external memory cache, by setting this flag as true, the user may save the RAM cost for running XGBoost within Spark

    missing

    the value represented the missing value in the dataset

    returns

    XGBoostModel when successful training

    Exceptions thrown

    ml.dmlc.xgboost4j.java.XGBoostError when the model training is failed

19. def trainWithDataFrame(trainingData: Dataset[_], params: Map[String, Any], round: Int, nWorkers: Int, obj: ObjectiveTrait = null, eval: EvalTrait = null, useExternalMemory: Boolean = false, missing: Float = Float.NaN, featureCol: String = "features", labelCol: String = "label"): XGBoostModel

    train XGBoost model with the DataFrame-represented data

    train XGBoost model with the DataFrame-represented data

    trainingData

    the trainingset represented as DataFrame

    params

    Map containing the parameters to configure XGBoost

    round

    the number of iterations

    nWorkers

    the number of xgboost workers, 0 by default which means that the number of workers equals to the partition number of trainingData RDD

    obj

    the user-defined objective function, null by default

    eval

    the user-defined evaluation function, null by default

    useExternalMemory

    indicate whether to use external memory cache, by setting this flag as true, the user may save the RAM cost for running XGBoost within Spark

    missing

    the value represented the missing value in the dataset

    featureCol

    the name of input column, "features" as default value

    labelCol

    the name of output column, "label" as default value

    returns

    XGBoostModel when successful training

    Annotations

    @throws( classOf[XGBoostError] )

    Exceptions thrown

    ml.dmlc.xgboost4j.java.XGBoostError when the model training is failed

20. def trainWithRDD(trainingData: RDD[org.apache.spark.ml.feature.LabeledPoint], params: Map[String, Any], round: Int, nWorkers: Int, obj: ObjectiveTrait = null, eval: EvalTrait = null, useExternalMemory: Boolean = false, missing: Float = Float.NaN): XGBoostModel

    various of train()

    various of train()

    trainingData

    the trainingset represented as RDD

    params

    Map containing the configuration entries

    round

    the number of iterations

    nWorkers

    the number of xgboost workers, 0 by default which means that the number of workers equals to the partition number of trainingData RDD

    obj

    the user-defined objective function, null by default

    eval

    the user-defined evaluation function, null by default

    useExternalMemory

    indicate whether to use external memory cache, by setting this flag as true, the user may save the RAM cost for running XGBoost within Spark

    missing

    the value represented the missing value in the dataset

    returns

    XGBoostModel when successful training

    Annotations

    @throws( classOf[XGBoostError] )

    Exceptions thrown

    ml.dmlc.xgboost4j.java.XGBoostError when the model training is failed

21. final def wait(): Unit

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

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23. final def wait(arg0: Long): Unit

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