case class Predictor(params: ModelParams, trees: Seq[DecisionTree], objective: Objective) extends Product with Serializable

Predictor fox XGboost

params

model parameters

trees

decision tree

objective

objective function

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@SerialVersionUID()
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  1. Predictor
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Instance Constructors

  1. new Predictor(params: ModelParams, trees: Seq[DecisionTree], objective: Objective)

    params

    model parameters

    trees

    decision tree

    objective

    objective function

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  6. final def eq(arg0: AnyRef): Boolean
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  7. final def getClass(): Class[_]
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  8. final def isInstanceOf[T0]: Boolean
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  9. final def ne(arg0: AnyRef): Boolean
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  10. final def notify(): Unit
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  11. final def notifyAll(): Unit
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  12. val objective: Objective
  13. val params: ModelParams
  14. def predict(values: Map[Integer, Double]): Array[Double]
  15. def predict(values: Array[Double]): Array[Double]
  16. def predict(values: Map[Int, Double]): Array[Double]

    Predict score

    Predict score

    values

    input data (sparse, first element is the position)

    returns

    scores (single element in case of binary classification)

  17. def predict(values: Array[Double]): Array[Double]

    Predict score

    Predict score

    values

    input data (dense). 0.0 is considered as N/A

    returns

    scores (single element in case of binary classification)

  18. def predict(vector: FVector): Array[Double]

    Predict score

    Predict score

    vector

    input FVector

    returns

    scores (single element in case of binary classification)

  19. def predictApproxContrib(values: Map[Integer, Double]): Array[Double]
  20. def predictApproxContrib(values: Array[Double]): Array[Double]
  21. def predictApproxContrib(values: Map[Int, Double]): Array[Double]

    Output feature contributions toward predictions of given data using approximation (means) algorithm

    Output feature contributions toward predictions of given data using approximation (means) algorithm

    values

    input data (sparse, first element is the position)

    returns

    feature contributions

  22. def predictApproxContrib(values: Array[Double]): Array[Double]

    Output feature contributions toward predictions of given data using approximation (means) algorithm

    Output feature contributions toward predictions of given data using approximation (means) algorithm

    values

    input data (dense). 0.0 is considered as N/A

    returns

    feature contributions

  23. def predictApproxContrib(vector: FVector): Array[Double]

    Output feature contributions toward predictions of given data using approximation (means) algorithm

    Output feature contributions toward predictions of given data using approximation (means) algorithm

    vector

    input FVector

    returns

    feature contributions

  24. def predictContrib(values: Map[Integer, Double]): Array[Double]
  25. def predictContrib(values: Array[Double]): Array[Double]
  26. def predictContrib(values: Map[Int, Double]): Array[Double]

    Output feature contributions toward predictions of given data using SHAP algorithm

    Output feature contributions toward predictions of given data using SHAP algorithm

    values

    input data (sparse, first element is the position)

    returns

    feature contributions

  27. def predictContrib(values: Array[Double]): Array[Double]

    Output feature contributions toward predictions of given data using SHAP algorithm

    Output feature contributions toward predictions of given data using SHAP algorithm

    values

    input data (dense). 0.0 is considered as N/A

    returns

    feature contributions

  28. def predictContrib(vector: FVector): Array[Double]

    Output feature contributions toward predictions of given data using SHAP algorithm

    Output feature contributions toward predictions of given data using SHAP algorithm

    vector

    input FVector

    returns

    feature contributions

  29. def serialize(): Array[Byte]

    Serialize the Predictor using internal format (this is NOT an Xgboost binary)

    Serialize the Predictor using internal format (this is NOT an Xgboost binary)

    returns

    array of bytes

  30. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  31. val trees: Seq[DecisionTree]
  32. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  33. final def wait(arg0: Long): Unit
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  34. final def wait(): Unit
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    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

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