LinearRegressionModel

Representation of the classical linear regression model y = intercept + a_0 x_0 + a_1 x_1 + ... + a_n x_n.

The length of the coefficients must match the length of the inputFeatures.

We use java.lang.Double for the type of the coefficients, because the scala Double type information is lost by scala/Gson and the deserialization fails badly for edge cases (e.g. Double.NaN).

coefficients: Vector of coefficients of the model. Must match the length of inputFeatures.
inputFeatures: Description of the (numeric) input features. Must match the length of coefficients.
intercept: Intercept of the model (defaults to 0).
dependentFeatureName: Name used to identify the dependent feature in an evaluation dataset.
identifier: Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.

Linear Supertypes

Product, Equals, RegressionRowModel, RowModel, MLModel, Serializable, Serializable, AnyRef, Any

Instance Constructors

new LinearRegressionModel(coefficients: Seq[Double], inputFeatures: Seq[ColumnDef], intercept: Double = 0, dependentFeatureName: String = "", identifier: String = "")

coefficients
Vector of coefficients of the model. Must match the length of inputFeatures.
inputFeatures
Description of the (numeric) input features. Must match the length of coefficients.
intercept
Intercept of the model (defaults to 0).
dependentFeatureName
Name used to identify the dependent feature in an evaluation dataset.
identifier
Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.

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
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( ... )
val coefficients: Seq[Double]

Vector of coefficients of the model.
Vector of coefficients of the model. Must match the length of inputFeatures.
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
LinearRegressionModel → RegressionRowModel
val dependentFeatureName: String

Name used to identify the dependent feature in an evaluation dataset.
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.

Definition Classes
LinearRegressionModel → RowModel
val inputFeatures: Seq[ColumnDef]

Description of the (numeric) input features.
Description of the (numeric) input features. Must match the length of coefficients.

Definition Classes
LinearRegressionModel → RowModel
val intercept: Double

Intercept of the model (defaults to 0).
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
RegressionRowModel → RowModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def transformationSchema: DetailedTransformationSchema

Definition Classes
RowModel
def transformer: LinearRegressionTransformer

Definition Classes
LinearRegressionModel → RegressionRowModel → 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 LinearRegressionModel(coefficients: Seq[Double], inputFeatures: Seq[ColumnDef], intercept: Double = 0, dependentFeatureName: String = "", identifier: String = "") extends RegressionRowModel with Product with Serializable

Instance Constructors

new LinearRegressionModel(coefficients: Seq[Double], inputFeatures: Seq[ColumnDef], intercept: Double = 0, dependentFeatureName: String = "", 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

def classesForLoading: Set[Class[_]]

def clone(): AnyRef

val coefficients: Seq[Double]

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]

val intercept: Double

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def outputFeatures: Seq[ColumnDef]

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

def transformationSchema: DetailedTransformationSchema

def transformer: LinearRegressionTransformer

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 RegressionRowModel

Inherited from RowModel

Inherited from MLModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped