org.apache.spark.mllib.regression

IsotonicRegressionModel

class IsotonicRegressionModel extends Serializable

:: Experimental ::

Regression model for isotonic regression.

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@Experimental()
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Instance Constructors

  1. new IsotonicRegressionModel(boundaries: Array[Double], predictions: Array[Double], isotonic: Boolean)

    boundaries

    Array of boundaries for which predictions are known. Boundaries must be sorted in increasing order.

    predictions

    Array of predictions associated to the boundaries at the same index. Results of isotonic regression and therefore monotone.

    isotonic

    indicates whether this is isotonic or antitonic.

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

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  7. val boundaries: Array[Double]

    Array of boundaries for which predictions are known.

    Array of boundaries for which predictions are known. Boundaries must be sorted in increasing order.

  8. def clone(): AnyRef

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  11. def finalize(): Unit

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

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  14. final def isInstanceOf[T0]: Boolean

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  15. val isotonic: Boolean

    indicates whether this is isotonic or antitonic.

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def predict(testData: Double): Double

    Predict a single label.

    Predict a single label. Using a piecewise linear function.

    testData

    Feature to be labeled.

    returns

    Predicted label. 1) If testData exactly matches a boundary then associated prediction is returned. In case there are multiple predictions with the same boundary then one of them is returned. Which one is undefined (same as java.util.Arrays.binarySearch). 2) If testData is lower or higher than all boundaries then first or last prediction is returned respectively. In case there are multiple predictions with the same boundary then the lowest or highest is returned respectively. 3) If testData falls between two values in boundary array then prediction is treated as piecewise linear function and interpolated value is returned. In case there are multiple values with the same boundary then the same rules as in 2) are used.

  20. def predict(testData: JavaDoubleRDD): JavaDoubleRDD

    Predict labels for provided features.

    Predict labels for provided features. Using a piecewise linear function.

    testData

    Features to be labeled.

    returns

    Predicted labels.

  21. def predict(testData: RDD[Double]): RDD[Double]

    Predict labels for provided features.

    Predict labels for provided features. Using a piecewise linear function.

    testData

    Features to be labeled.

    returns

    Predicted labels.

  22. val predictions: Array[Double]

    Array of predictions associated to the boundaries at the same index.

    Array of predictions associated to the boundaries at the same index. Results of isotonic regression and therefore monotone.

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

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  24. def toString(): String

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