org.allenai.nlpstack.parse.poly.decisiontree

DecisionTreeTrainer

Related Doc: package decisiontree

class DecisionTreeTrainer extends ProbabilisticClassifierTrainer

A DecisionTreeTrainer trains decision trees from data.

Linear Supertypes
ProbabilisticClassifierTrainer, (FeatureVectorSource) ⇒ ProbabilisticClassifier, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. DecisionTreeTrainer
  2. ProbabilisticClassifierTrainer
  3. Function1
  4. AnyRef
  5. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new DecisionTreeTrainer(validationPercentage: Float, informationGainMetric: InformationGainMetric, featuresExaminedPerNode: Float = 1.0f, maximumDepth: Int = Integer.MAX_VALUE)

    validationPercentage

    the percentage of data to "hold out" for pruning

    informationGainMetric

    the information gain metric to use ("entropy" or "multinomial")

    featuresExaminedPerNode

    for each node, the fraction of features to randomly consider as potential splitting features

    maximumDepth

    the maximum desired depth of the trained decision tree

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. def andThen[A](g: (ProbabilisticClassifier) ⇒ A): (FeatureVectorSource) ⇒ A

    Definition Classes
    Function1
    Annotations
    @unspecialized()

  5. def apply(data: FeatureVectorSource): ProbabilisticClassifier

    Factory constructor of DecisionTree.

    Factory constructor of DecisionTree.

    Randomly splits data into training:validation (according to the validationPercentage param). Does reduced-error pruning on validation data. Uses a uniform prior over training labels where each label is assumed to have been seen once already. This Laplace smoothing affects the probability distribution over labels for each feature.

    data

    training and validation data

    returns

    the induced decision tree

    Definition Classes
    DecisionTreeTrainer → Function1

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

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

  8. def compose[A](g: (A) ⇒ FeatureVectorSource): (A) ⇒ ProbabilisticClassifier

    Definition Classes
    Function1
    Annotations
    @unspecialized()

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

    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  11. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  16. final def notify(): Unit

    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  19. def toString(): String

    Definition Classes
    Function1 → AnyRef → Any

  20. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from ProbabilisticClassifierTrainer

Inherited from (FeatureVectorSource) ⇒ ProbabilisticClassifier

Inherited from AnyRef

Inherited from Any

Ungrouped