org.apache.spark.mllib.regression

RidgeRegressionModel

class RidgeRegressionModel extends GeneralizedLinearModel with RegressionModel with Serializable

Regression model trained using RidgeRegression.

Linear Supertypes
RegressionModel, GeneralizedLinearModel, Serializable, Serializable, AnyRef, Any
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  1. RidgeRegressionModel
  2. RegressionModel
  3. GeneralizedLinearModel
  4. Serializable
  5. Serializable
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  7. Any
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  1. final def !=(arg0: AnyRef): Boolean

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    AnyRef
  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  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.

    Intercept computed for this model.

    Definition Classes
    RidgeRegressionModelGeneralizedLinearModel
  14. final def isInstanceOf[T0]: Boolean

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

    Definition Classes
    AnyRef
  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  18. def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

    Predict values for examples stored in a JavaRDD.

    Predict values for examples stored in a JavaRDD.

    testData

    JavaRDD representing data points to be predicted

    returns

    a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction

    Definition Classes
    RegressionModel
  19. 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

    Definition Classes
    GeneralizedLinearModel
  20. 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

    Definition Classes
    GeneralizedLinearModel
  21. 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
    Definition Classes
    RidgeRegressionModelGeneralizedLinearModel
  22. final def synchronized[T0](arg0: ⇒ T0): T0

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  23. def toString(): String

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  24. final def wait(): Unit

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

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

    Definition Classes
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    @throws( ... )
  27. val weights: Vector

    Weights computed for every feature.

    Weights computed for every feature.

    Definition Classes
    RidgeRegressionModelGeneralizedLinearModel

Inherited from RegressionModel

Inherited from GeneralizedLinearModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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