org.apache.spark.mllib.regression

GeneralizedLinearModel

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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@DeveloperApi()
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Known Subclasses
LassoModel, LinearRegressionModel, LogisticRegressionModel, RidgeRegressionModel, SVMModel
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Instance Constructors

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

    weights

    Weights computed for every feature.

    intercept

    Intercept computed for this model.

Abstract Value Members

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

    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: AnyRef): Boolean

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

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  12. def hashCode(): Int

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  13. val intercept: Double

    Intercept computed for this model.

  14. final def isInstanceOf[T0]: Boolean

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def predict(testData: Vector): Double

    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

  19. def predict(testData: RDD[Vector]): RDD[Double]

    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

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

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  21. def toString(): String

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

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  25. val weights: Vector

    Weights computed for every feature.

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