Packages

c

io.citrine.lolo.learners

ExtraRandomTrees

case class ExtraRandomTrees(numTrees: Int = -1, useJackknife: Boolean = false, biasLearner: Option[Learner] = None, leafLearner: Option[Learner] = None, subsetStrategy: Any = "auto", minLeafInstances: Int = 1, maxDepth: Int = Integer.MAX_VALUE, uncertaintyCalibration: Boolean = false, disableBootstrap: Boolean = true, randomlyRotateFeatures: Boolean = false, rng: Random = Random) extends Learner with Product with Serializable

Extremely randomized tree ensemble

This is based on Geurts, P., Ernst, D. & Wehenkel, L. Extremely randomized trees. Mach Learn 63, 3–42 (2006). https://doi.org/10.1007/s10994-006-6226-1.

numTrees

number of trees to use (-1 => number of training instances)

useJackknife

whether to use jackknife based variance estimates

biasLearner

learner to model bias (absolute residual)

leafLearner

learner to use at the leaves of the trees

subsetStrategy

for random feature selection at each split (auto => all features for regression)

minLeafInstances

minimum number of instances per leave in each tree

maxDepth

maximum depth of each tree in the forest (default: unlimited)

uncertaintyCalibration

whether to empirically recalibrate the predicted uncertainties (default: false)

disableBootstrap

whether to disable bootstrap (default: true)

randomlyRotateFeatures

whether to randomly rotate real features for each tree in the forest (default: false)

rng

random number generator to use

Linear Supertypes
Product, Equals, Learner, Serializable, AnyRef, Any
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  1. ExtraRandomTrees
  2. Product
  3. Equals
  4. Learner
  5. Serializable
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Instance Constructors

  1. new ExtraRandomTrees(numTrees: Int = -1, useJackknife: Boolean = false, biasLearner: Option[Learner] = None, leafLearner: Option[Learner] = None, subsetStrategy: Any = "auto", minLeafInstances: Int = 1, maxDepth: Int = Integer.MAX_VALUE, uncertaintyCalibration: Boolean = false, disableBootstrap: Boolean = true, randomlyRotateFeatures: Boolean = false, rng: Random = Random)

    numTrees

    number of trees to use (-1 => number of training instances)

    useJackknife

    whether to use jackknife based variance estimates

    biasLearner

    learner to model bias (absolute residual)

    leafLearner

    learner to use at the leaves of the trees

    subsetStrategy

    for random feature selection at each split (auto => all features for regression)

    minLeafInstances

    minimum number of instances per leave in each tree

    maxDepth

    maximum depth of each tree in the forest (default: unlimited)

    uncertaintyCalibration

    whether to empirically recalibrate the predicted uncertainties (default: false)

    disableBootstrap

    whether to disable bootstrap (default: true)

    randomlyRotateFeatures

    whether to randomly rotate real features for each tree in the forest (default: false)

    rng

    random number generator to use

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. val biasLearner: Option[Learner]
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  7. val disableBootstrap: Boolean
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  10. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. val leafLearner: Option[Learner]
  13. val maxDepth: Int
  14. val minLeafInstances: Int
  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  17. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  18. val numTrees: Int
  19. def productElementNames: Iterator[String]
    Definition Classes
    Product
  20. val randomlyRotateFeatures: Boolean
  21. val rng: Random
  22. val subsetStrategy: Any
  23. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  24. def train(trainingData: Seq[(Vector[Any], Any)], weights: Option[Seq[Double]]): TrainingResult

    Train an extremely randomized tree ensemble model

    Train an extremely randomized tree ensemble model

    trainingData

    to train on

    weights

    for the training rows, if applicable

    returns

    training result containing a model

    Definition Classes
    ExtraRandomTrees → Learner
  25. def train(trainingData: Seq[(Vector[Any], Any, Double)]): TrainingResult

    Train a model with weights

    Train a model with weights

    trainingData

    with weights in the form (features, label, weight)

    returns

    training result containing a model

    Definition Classes
    Learner
  26. val uncertaintyCalibration: Boolean
  27. val useJackknife: Boolean
  28. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  29. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  30. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

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Inherited from Learner

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