ml.combust.mleap.core.regression

RandomForestRegressionModel

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

Class for random forest regression.

trees

trees in the random forest

numFeatures

number of features needed for prediction

Linear Supertypes
Serializable, Serializable, Product, Equals, Model, TreeEnsemble, AnyRef, Any
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  1. RandomForestRegressionModel
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Model
  7. TreeEnsemble
  8. AnyRef
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Instance Constructors

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

    trees

    trees in the random forest

    numFeatures

    number of features needed for prediction

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.RandomForestRegressionModel#predict.

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

    features

    feature for prediction

    returns

    prediction

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  12. def inputSchema: StructType

    Definition Classes
    RandomForestRegressionModel → Model

  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  15. final def notify(): Unit

    Definition Classes
    AnyRef

  16. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  17. val numFeatures: Int

    number of features needed for prediction

  18. def numTrees: Int

    Number of trees in the ensemble

    Number of trees in the ensemble

    Definition Classes
    TreeEnsemble

  19. def outputSchema: StructType

    Definition Classes
    RandomForestRegressionModel → Model

  20. def predict(features: Vector): Double

    Predict a value with the forest.

    Predict a value with the forest.

    features

    features for prediction

    returns

    prediction

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

    Definition Classes
    AnyRef

  22. val treeWeights: Seq[Double]

    Weights for each tree.

    Weights for each tree.

    Definition Classes
    RandomForestRegressionModel → TreeEnsemble

  23. val trees: Seq[DecisionTreeRegressionModel]

    trees in the random forest

    trees in the random forest

    Definition Classes
    RandomForestRegressionModel → TreeEnsemble

  24. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Model

Inherited from TreeEnsemble

Inherited from AnyRef

Inherited from Any

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