LinearRegressionModel

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

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

   

   Definition Classes

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4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

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   @throws( ... )

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

   

   Definition Classes

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7. def equals(arg0: Any): Boolean

   

   Definition Classes

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8. def finalize(): Unit

   

   Attributes

   protected[java.lang]

   Definition Classes

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

9. def formatVersion: String

   

   Current version of model save/load format.

   Current version of model save/load format.

   Attributes

   protected

   Definition Classes

   LinearRegressionModel → Saveable

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

    

    Definition Classes

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11. def hashCode(): Int

    

    Definition Classes

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12. val intercept: Double

    

    Intercept computed for this model.

    Intercept computed for this model.

    Definition Classes

    LinearRegressionModel → GeneralizedLinearModel

    Annotations

    @Since( "0.8.0" )

13. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

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15. final def notify(): Unit

    

    Definition Classes

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16. final def notifyAll(): Unit

    

    Definition Classes

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17. 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

    Annotations

    @Since( "1.0.0" )

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

    Definition Classes

    GeneralizedLinearModel

    Annotations

    @Since( "1.0.0" )

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

    Definition Classes

    GeneralizedLinearModel

    Annotations

    @Since( "1.0.0" )

20. 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

    LinearRegressionModel → GeneralizedLinearModel

21. def save(sc: SparkContext, path: String): Unit

    

    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    Definition Classes

    LinearRegressionModel → Saveable

    Annotations

    @Since( "1.3.0" )

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

    

    Definition Classes

    AnyRef

23. def toPMML(): String

    

    Export the model to a String in PMML format

    Export the model to a String in PMML format

    Definition Classes

    PMMLExportable

    Annotations

    @Since( "1.4.0" )

24. def toPMML(outputStream: OutputStream): Unit

    

    Export the model to the OutputStream in PMML format

    Export the model to the OutputStream in PMML format

    Definition Classes

    PMMLExportable

    Annotations

    @Since( "1.4.0" )

25. def toPMML(sc: SparkContext, path: String): Unit

    

    Export the model to a directory on a distributed file system in PMML format

    Export the model to a directory on a distributed file system in PMML format

    Definition Classes

    PMMLExportable

    Annotations

    @Since( "1.4.0" )

26. def toPMML(localPath: String): Unit

    

    Export the model to a local file in PMML format

    Export the model to a local file in PMML format

    Definition Classes

    PMMLExportable

    Annotations

    @Since( "1.4.0" )

27. def toString(): String

    

    Print a summary of the model.

    Print a summary of the model.

    Definition Classes

    GeneralizedLinearModel → AnyRef → Any

28. final def wait(): Unit

    

    Definition Classes

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    @throws( ... )

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

    

    Definition Classes

    AnyRef

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    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

31. val weights: Vector

    

    Weights computed for every feature.

    Weights computed for every feature.

    Definition Classes

    LinearRegressionModel → GeneralizedLinearModel

    Annotations

    @Since( "1.0.0" )