MultiLogisticRegressionModel

Instance Constructors

new MultiLogisticRegressionModel(singleLORs: Seq[SingleLogisticRegression], baseValue: String, dependentFeatureName: String, inputFeatures: Seq[ColumnDef], identifier: String = "")

Value Members

final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def !=(arg0: Any): Boolean

Definition Classes
Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def ==(arg0: Any): Boolean

Definition Classes
Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val baseValue: String
lazy val classLabels: List[String]

Definition Classes
MultiLogisticRegressionModel → CategoricalRowModel
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
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def dependentFeature: ColumnDef

Used when we are doing model quality evaluation e.
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
val dependentFeatureName: String
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
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
val inputFeatures: Seq[ColumnDef]

Definition Classes
MultiLogisticRegressionModel → RowModel
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def outputFeatures: Seq[ColumnDef]

Definition Classes
ClassificationRowModel → RowModel
val singleLORs: Seq[SingleLogisticRegression]
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def transformationSchema: DetailedTransformationSchema

Definition Classes
RowModel
def transformer: LogisticRegressionTransformer

Definition Classes
MultiLogisticRegressionModel → ClassificationRowModel → CategoricalRowModel → RowModel
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

case class MultiLogisticRegressionModel(singleLORs: Seq[SingleLogisticRegression], baseValue: String, dependentFeatureName: String, inputFeatures: Seq[ColumnDef], identifier: String = "") extends ClassificationRowModel with Product with Serializable

Instance Constructors

new MultiLogisticRegressionModel(singleLORs: Seq[SingleLogisticRegression], baseValue: String, dependentFeatureName: String, inputFeatures: Seq[ColumnDef], identifier: String = "")

Value Members

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val baseValue: String

lazy val classLabels: List[String]

def classesForLoading: Set[Class[_]]

def clone(): AnyRef

def dependentFeature: ColumnDef

val dependentFeatureName: String

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

val identifier: String

val inputFeatures: Seq[ColumnDef]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def outputFeatures: Seq[ColumnDef]

val singleLORs: Seq[SingleLogisticRegression]

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

def transformationSchema: DetailedTransformationSchema

def transformer: LogisticRegressionTransformer

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from ClassificationRowModel

Inherited from CategoricalRowModel

Inherited from RowModel

Inherited from MLModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped