LinearRegressionModel

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

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

   Definition Classes

   AnyRef

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

   Definition Classes

   Any

6. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

7. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

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   Annotations

   @throws( ... )

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

   Definition Classes

   AnyRef

9. def equals(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

10. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

11. def formatVersion: String

    Current version of model save/load format.

    Current version of model save/load format.

    Attributes

    protected

    Definition Classes

    LinearRegressionModel → Saveable

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

    Definition Classes

    AnyRef → Any

13. def hashCode(): Int

    Definition Classes

    AnyRef → Any

14. val intercept: Double

    Intercept computed for this model.

    Intercept computed for this model.

    Definition Classes

    LinearRegressionModel → GeneralizedLinearModel

    Annotations

    @Since( "0.8.0" )

15. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Definition Classes

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17. final def notify(): Unit

    Definition Classes

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18. final def notifyAll(): Unit

    Definition Classes

    AnyRef

19. 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" )

20. 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" )

21. 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" )

22. 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

23. 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" )

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

    Definition Classes

    AnyRef

25. def toPMML(): String

    :: Experimental :: Export the model to a String in PMML format

    :: Experimental :: Export the model to a String in PMML format

    Definition Classes

    PMMLExportable

    Annotations

    @Experimental() @Since( "1.4.0" )

26. def toPMML(outputStream: OutputStream): Unit

    :: Experimental :: Export the model to the OutputStream in PMML format

    :: Experimental :: Export the model to the OutputStream in PMML format

    Definition Classes

    PMMLExportable

    Annotations

    @Experimental() @Since( "1.4.0" )

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

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

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

    Definition Classes

    PMMLExportable

    Annotations

    @Experimental() @Since( "1.4.0" )

28. def toPMML(localPath: String): Unit

    :: Experimental :: Export the model to a local file in PMML format

    :: Experimental :: Export the model to a local file in PMML format

    Definition Classes

    PMMLExportable

    Annotations

    @Experimental() @Since( "1.4.0" )

29. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

    Definition Classes

    GeneralizedLinearModel → AnyRef → Any

30. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

33. val weights: Vector

    Weights computed for every feature.

    Weights computed for every feature.

    Definition Classes

    LinearRegressionModel → GeneralizedLinearModel

    Annotations

    @Since( "1.0.0" )