ml.combust.mleap.core.regression

GBTRegressionModel

case class GBTRegressionModel(trees: Seq[DecisionTreeRegressionModel], treeWeights: Seq[Double], numFeatures: Int) extends TreeEnsemble with Serializable with Product

Class for gradient boosted tree regression model.

trees

trees in model

treeWeights

weight of each tree

numFeatures

number of features

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Product, Equals, Serializable, Serializable, TreeEnsemble, AnyRef, Any
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Instance Constructors

  1. new GBTRegressionModel(trees: Seq[DecisionTreeRegressionModel], treeWeights: Seq[Double], numFeatures: Int)

    trees

    trees in model

    treeWeights

    weight of each tree

    numFeatures

    number of features

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. def apply(features: Vector): Double

    Alias for ml.combust.mleap.core.regression.GBTRegressionModel#predict

    Alias for ml.combust.mleap.core.regression.GBTRegressionModel#predict

    features

    features to predict

    returns

    prediction

  7. final def asInstanceOf[T0]: T0

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    Any

  8. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

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

    Definition Classes
    AnyRef

  10. def finalize(): Unit

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    protected[java.lang]
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  11. final def getClass(): Class[_]

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  12. final def isInstanceOf[T0]: Boolean

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

    Definition Classes
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  14. final def notify(): Unit

    Definition Classes
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  15. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  16. val numFeatures: Int

    number of features

  17. def numTrees: Int

    Number of trees in the ensemble

    Number of trees in the ensemble

    Definition Classes
    TreeEnsemble

  18. def predict(features: Vector): Double

    Make prediction based on feature vector.

    Make prediction based on feature vector.

    features

    features to predict

    returns

    prediction

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

    Definition Classes
    AnyRef

  20. val treeWeights: Seq[Double]

    weight of each tree

    weight of each tree

    Definition Classes
    GBTRegressionModel → TreeEnsemble

  21. val trees: Seq[DecisionTreeRegressionModel]

    trees in model

    trees in model

    Definition Classes
    GBTRegressionModel → TreeEnsemble

  22. final def wait(): Unit

    Definition Classes
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    @throws( ... )

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

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

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