Class

org.apache.spark.mllib.regression

IsotonicRegression

Related Doc: package regression

Permalink

class IsotonicRegression extends Serializable

Isotonic regression. Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.

Sequential PAV implementation based on: Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani. "Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61. Available from here

Sequential PAV parallelization based on: Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset. "An approach to parallelizing isotonic regression." Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147. Available from here

Annotations
@Since( "1.3.0" )
Source
IsotonicRegression.scala
See also

Isotonic regression (Wikipedia)

Linear Supertypes
Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. IsotonicRegression
  2. Serializable
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new IsotonicRegression()

    Permalink

    Constructs IsotonicRegression instance with default parameter isotonic = true.

    Constructs IsotonicRegression instance with default parameter isotonic = true.

    Annotations
    @Since( "1.3.0" )

Value Members

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

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  5. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  7. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  10. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  11. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  12. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  13. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  14. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  15. def run(input: JavaRDD[(Double, Double, Double)]): IsotonicRegressionModel

    Permalink

    Run pool adjacent violators algorithm to obtain isotonic regression model.

    Run pool adjacent violators algorithm to obtain isotonic regression model.

    input

    JavaRDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are ordered before the algorithm is executed.

    returns

    Isotonic regression model.

    Annotations
    @Since( "1.3.0" )
  16. def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel

    Permalink

    Run IsotonicRegression algorithm to obtain isotonic regression model.

    Run IsotonicRegression algorithm to obtain isotonic regression model.

    input

    RDD of tuples (label, feature, weight) where label is dependent variable for which we calculate isotonic regression, feature is independent variable and weight represents number of measures with default 1. If multiple labels share the same feature value then they are ordered before the algorithm is executed.

    returns

    Isotonic regression model.

    Annotations
    @Since( "1.3.0" )
  17. def setIsotonic(isotonic: Boolean): IsotonicRegression.this.type

    Permalink

    Sets the isotonic parameter.

    Sets the isotonic parameter.

    isotonic

    Isotonic (increasing) or antitonic (decreasing) sequence.

    returns

    This instance of IsotonicRegression.

    Annotations
    @Since( "1.3.0" )
  18. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  19. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  20. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  21. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped