Class

org.apache.spark.mllib.regression

GeneralizedLinearModel

Related Doc: package regression

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abstract class GeneralizedLinearModel extends Serializable

:: DeveloperApi :: GeneralizedLinearModel (GLM) represents a model trained using GeneralizedLinearAlgorithm. GLMs consist of a weight vector and an intercept.

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@Since( "0.8.0" ) @DeveloperApi()
Source
GeneralizedLinearAlgorithm.scala
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Instance Constructors

  1. new GeneralizedLinearModel(weights: Vector, intercept: Double)

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    weights

    Weights computed for every feature.

    intercept

    Intercept computed for this model.

    Annotations
    @Since( "1.0.0" )

Abstract Value Members

  1. abstract def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double

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    Predict the result given a data point and the weights learned.

    Predict the result given a data point and the weights learned.

    dataMatrix

    Row vector containing the features for this data point

    weightMatrix

    Column vector containing the weights of the model

    intercept

    Intercept of the model.

    Attributes
    protected

Concrete Value Members

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def hashCode(): Int

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  11. val intercept: Double

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    Intercept computed for this model.

    Intercept computed for this model.

    Annotations
    @Since( "0.8.0" )
  12. final def isInstanceOf[T0]: Boolean

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def predict(testData: Vector): Double

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    Predict values for a single data point using the model trained.

    Predict values for a single data point using the model trained.

    testData

    array representing a single data point

    returns

    Double prediction from the trained model

    Annotations
    @Since( "1.0.0" )
  17. def predict(testData: RDD[Vector]): RDD[Double]

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    Predict values for the given data set using the model trained.

    Predict values for the given data set using the model trained.

    testData

    RDD representing data points to be predicted

    returns

    RDD[Double] where each entry contains the corresponding prediction

    Annotations
    @Since( "1.0.0" )
  18. final def synchronized[T0](arg0: ⇒ T0): T0

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  19. def toString(): String

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    Print a summary of the model.

    Print a summary of the model.

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

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

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

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  23. val weights: Vector

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    Weights computed for every feature.

    Weights computed for every feature.

    Annotations
    @Since( "1.0.0" )

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