ModelSelectorSummary

This is used to store all information about fitting and model selection generated by the model selector class

validationType: type of validation performed to select hyper parameters
validationParameters: parameters on validation
dataPrepParameters: parameters on data preparation before hyper parameter tuning
dataPrepResults: changes made to the data in data preparation
evaluationMetric: metric used to select hyper parameters and model
problemType: type of modeling (eg binary classification, regressionm etc)
bestModelUID: best model UID
validationResults: model with parameters and metric for all evaluated
trainEvaluation: winning model performance on training data set
holdoutEvaluation: winning model performance on holdout data set

Linear Supertypes

Serializable, Serializable, Product, Equals, MetadataLike, AnyRef, Any

Instance Constructors

new ModelSelectorSummary(validationType: ValidationType, validationParameters: Map[String, Any], dataPrepParameters: Map[String, Any], dataPrepResults: Option[SplitterSummary], evaluationMetric: EvalMetric, problemType: ProblemType, bestModelUID: String, bestModelName: String, bestModelType: String, validationResults: Seq[ModelEvaluation], trainEvaluation: EvaluationMetrics, holdoutEvaluation: Option[EvaluationMetrics])

validationType

type of validation performed to select hyper parameters

validationParameters

parameters on validation

dataPrepParameters

parameters on data preparation before hyper parameter tuning

dataPrepResults

changes made to the data in data preparation

evaluationMetric

metric used to select hyper parameters and model

problemType

type of modeling (eg binary classification, regressionm etc)

bestModelUID

best model UID

validationResults

model with parameters and metric for all evaluated

trainEvaluation

winning model performance on training data set

holdoutEvaluation

winning model performance on holdout data set

Value Members

final def !=(arg0: Any): Boolean

Definition Classes

AnyRef → Any
final def ##(): Int

Definition Classes

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

Definition Classes

AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes

Any
val bestModelName: String
val bestModelType: String
val bestModelUID: String

best model UID
def clone(): AnyRef

Attributes

protected[java.lang]

Definition Classes

AnyRef

Annotations

@throws( ... )
val dataPrepParameters: Map[String, Any]

parameters on data preparation before hyper parameter tuning
val dataPrepResults: Option[SplitterSummary]

changes made to the data in data preparation
final def eq(arg0: AnyRef): Boolean

Definition Classes

AnyRef
val evaluationMetric: EvalMetric

metric used to select hyper parameters and model
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 holdoutEvaluation: Option[EvaluationMetrics]

winning model performance on holdout data set
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
val problemType: ProblemType

type of modeling (eg binary classification, regressionm etc)
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes

AnyRef
def toMetadata(skipUnsupported: Boolean): Metadata

Converts to Metadata

Converts to Metadata

skipUnsupported

skip unsupported values

returns

Metadata metadata

Definition Classes

ModelSelectorSummary → MetadataLike

Exceptions thrown

RuntimeException in case of unsupported value type
def toMetadata(): Metadata

Converts to Metadata

Converts to Metadata

returns

Metadata metadata

Definition Classes

MetadataLike

Exceptions thrown

RuntimeException in case of unsupported value type
val trainEvaluation: EvaluationMetrics

winning model performance on training data set
val validationParameters: Map[String, Any]

parameters on validation
val validationResults: Seq[ModelEvaluation]

model with parameters and metric for all evaluated
val validationType: ValidationType

type of validation performed to select hyper parameters
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( ... )

Related Docs: object ModelSelectorSummary | package selector

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val bestModelName: String

val bestModelType: String

val bestModelUID: String

def clone(): AnyRef

val dataPrepParameters: Map[String, Any]

val dataPrepResults: Option[SplitterSummary]

final def eq(arg0: AnyRef): Boolean

val evaluationMetric: EvalMetric

def finalize(): Unit

final def getClass(): Class[_]

val holdoutEvaluation: Option[EvaluationMetrics]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val problemType: ProblemType

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

def toMetadata(skipUnsupported: Boolean): Metadata

def toMetadata(): Metadata

val trainEvaluation: EvaluationMetrics

val validationParameters: Map[String, Any]

val validationResults: Seq[ModelEvaluation]

val validationType: ValidationType

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from MetadataLike

Inherited from AnyRef

Inherited from Any

Ungrouped