c

io.citrine.lolo.linear

GuessTheMeanModel

class GuessTheMeanModel[T] extends Model[GuessTheMeanResult[T]]

Linear Supertypes
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. GuessTheMeanModel
  2. Model
  3. Serializable
  4. AnyRef
  5. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new GuessTheMeanModel(mean: T)

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. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  7. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  11. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  14. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  15. def shapley(input: Vector[Any], omitFeatures: Set[Int] = Set()): Option[DenseMatrix[Double]]

    Compute Shapley feature attributions for a given input in this node's subtree

    Compute Shapley feature attributions for a given input in this node's subtree

    input

    for which to compute feature attributions.

    omitFeatures

    feature indices to omit in computing Shapley values

    returns

    matrix of attributions for each feature and output One row per feature, each of length equal to the output dimension. The output dimension is 1 for single-task regression, or equal to the number of classification categories.

    Definition Classes
    Model
  16. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  17. def toString(): String
    Definition Classes
    AnyRef → Any
  18. def transform(inputs: Seq[Vector[Any]]): GuessTheMeanResult[T]

    Apply the model to a seq of inputs

    Apply the model to a seq of inputs

    inputs

    to apply the model to

    returns

    a PredictionResult which includes, at least, the expected outputs

    Definition Classes
    GuessTheMeanModelModel
  19. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  20. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  21. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()

Inherited from Model[GuessTheMeanResult[T]]

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped