MultiLogisticRegressionModel

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

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

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. val baseValue: String

   

6. lazy val classLabels: List[String]

   

   The total set of classes that the predicted value can take on.

   The total set of classes that the predicted value can take on.

   returns

   The set of possible values for the predicted value.

   Definition Classes

   MultiLogisticRegressionModel → CategoricalRowModel

7. def classesForLoading: Set[Class[_]]

   

   This should return a representative class from every jar that is needed to load the object during deserialization.

   This should return a representative class from every jar that is needed to load the object during deserialization.

   Default implementation returns the class of MLModel, and the class of the model implementation.

   Definition Classes

   MLModel

8. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

9. def dependentFeature: ColumnDef

   

   Used when we are doing model quality evaluation e.g.

   Used when we are doing model quality evaluation e.g. Confusion Matrix, so we know what feature in the test dataset to compare the result to.

   returns

   Feature description used to identify the dependent feature in an evaluation dataset.

   Definition Classes

   MultiLogisticRegressionModel → ClassificationRowModel

10. val dependentFeatureName: String

    

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

    

    Definition Classes

    AnyRef

12. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

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

    

    Definition Classes

    AnyRef → Any

14. def getPFAConverter: PFAConverter

    

    Definition Classes

    MultiLogisticRegressionModel → PFAConvertible

15. val identifier: String

    

    Used to identify this model when in a collection of models.

    Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.

    returns

    identifier for the model.

    Definition Classes

    MultiLogisticRegressionModel → RowModel

16. val inputFeatures: Seq[ColumnDef]

    

    Definition Classes

    MultiLogisticRegressionModel → RowModel

17. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

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

    

    Definition Classes

    AnyRef

19. final def notify(): Unit

    

    Definition Classes

    AnyRef

20. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

21. def outputFeatures: Seq[ColumnDef]

    

    Definition Classes

    ClassificationRowModel → RowModel

22. val singleLORs: Seq[SingleLogisticRegression]

    

23. def sqlOutputFeatures: Seq[ColumnDef]

    

    Definition Classes

    CategoricalRowModel → RowModel

24. def sqlTransformer(sqlGenerator: SQLGenerator): Some[LogisticRegressionSQLTransformer]

    

    Definition Classes

    MultiLogisticRegressionModel → ClassificationRowModel → RowModel

25. def streamline(requiredOutputFeatureNames: Seq[String]): ClassificationRowModel

    

    Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.

    Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.

    Behaviour is undefined if requiredOutputFeatureNames is not actually a subset of outputFeatures.map(_columnName).

    The resulting model may still have more output features than those required (i.e. for ClassificationRowModel implementations which always have the same output features, or for OneHotEncodingModel whose output features naturally group together).

    requiredOutputFeatureNames

    names of the output features that the new model should produce.

    returns

    A model which has the same functionality for those output features listed, but potentially fewer input features.

    Definition Classes

    ClassificationRowModel → RowModel

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

    

    Definition Classes

    AnyRef

27. def transformationSchema: DetailedTransformationSchema

    

    Definition Classes

    RowModel

28. def transformer: LogisticRegressionTransformer

    

    Definition Classes

    MultiLogisticRegressionModel → ClassificationRowModel → CategoricalRowModel → RowModel

29. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )