SVMModel

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 clearThreshold(): SVMModel.this.type

   :: Experimental :: Clears the threshold so that predict will output raw prediction scores.

   :: Experimental :: Clears the threshold so that predict will output raw prediction scores.

   Annotations

   @Experimental()

8. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

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

   Definition Classes

   AnyRef

10. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

11. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    SVMModel → GeneralizedLinearModel

15. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Definition Classes

    AnyRef

17. final def notify(): Unit

    Definition Classes

    AnyRef

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

    ClassificationModel

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

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

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

    SVMModel → GeneralizedLinearModel

23. def setThreshold(threshold: Double): SVMModel.this.type

    :: Experimental :: Sets the threshold that separates positive predictions from negative predictions.

    :: Experimental :: Sets the threshold that separates positive predictions from negative predictions. An example with prediction score greater than or equal to this threshold is identified as an positive, and negative otherwise. The default value is 0.0.

    Annotations

    @Experimental()

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

    Definition Classes

    AnyRef

25. def toString(): String

    Definition Classes

    AnyRef → Any

26. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

29. val weights: Vector

    Weights computed for every feature.

    Weights computed for every feature.

    Definition Classes

    SVMModel → GeneralizedLinearModel