ml.dmlc.xgboost4j.scala

# XGBoost

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Related Doc: package scala

#### object XGBoost

XGBoost Scala Training function.

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6. #### def crossValidation(data: DMatrix, params: Map[String, Any], round: Int, nfold: Int = 5, metrics: Array[String] = null, obj: ObjectiveTrait = null, eval: EvalTrait = null): Array[String]

   Cross-validation with given parameters.

   Cross-validation with given parameters.

   data

     Data to be trained.

   params

     Booster params.

   round

     Number of boosting iterations.

   nfold

     Number of folds in CV.

   metrics

     Evaluation metrics to be watched in CV.

   obj

     customized objective

   eval

     customized evaluation

   returns

     evaluation history

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     @throws( classOf[XGBoostError] )

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13. #### def loadModel(in: InputStream): Booster

    Load a new Booster model from a file opened as input stream.

    Load a new Booster model from a file opened as input stream. The assumption is the input stream only contains one XGBoost Model. This can be used to load existing booster models saved by other XGBoost bindings.

    in

      The input stream of the file.

    returns

      The create booster

    Annotations
      @throws( classOf[XGBoostError] )

14. #### def loadModel(modelPath: String): Booster

    load model from modelPath

    load model from modelPath

    modelPath

      booster modelPath

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      @throws( classOf[XGBoostError] )

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20. #### def train(dtrain: DMatrix, params: Map[String, Any], round: Int, watches: Map[String, DMatrix] = Map(), metrics: Array[Array[Float]] = null, obj: ObjectiveTrait = null, eval: EvalTrait = null, earlyStoppingRound: Int = 0, booster: Booster = null): Booster

    Train a booster given parameters.

    Train a booster given parameters.

    dtrain

      Data to be trained.

    params

      Parameters.

    round

      Number of boosting iterations.

    watches

      a group of items to be evaluated during training, this allows user to watch performance on the validation set.

    metrics

      array containing the evaluation metrics for each matrix in watches for each iteration

    obj

      customized objective

    eval

      customized evaluation

    earlyStoppingRound

      if non-zero, training would be stopped after a specified number of consecutive increases in any evaluation metric.

    booster

      train from scratch if set to null; train from an existing booster if not null.

    returns

      The trained booster.

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      @throws( classOf[XGBoostError] )

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