Insights

Summary of insights for a derived feature

derivedFeatureName: name of derived feature
stagesApplied: the stageNames of all stages applied to make feature from the raw input feature
derivedFeatureGroup: grouping of this feature if the feature is a pivot
derivedFeatureValue: value of the feature if the feature is a numeric encoding of a non-numeric feature or bucket
excluded: was this derived feature excluded from the model by the sanity checker
corr: the correlation of this feature with the label
cramersV: the cramersV of this feature with the label (when both label and feature are categorical)
mutualInformation: the mutual information for this feature (and all features in its grouping) with the label (categorical features only)
pointwiseMutualInformation: the mutual information of this feature with each value of the label (categorical features only)
countMatrix: the counts of the occurrence of this feature with each of the label values (categorical features only)
contribution: the contribution of this feature to the model (eg feature importance for random forest, weight for logistic regression)
min: the min value of this feature
max: the max value of this feature
mean: the mean value of this feature
variance: the variance of this feature

Linear Supertypes

Serializable, Serializable, Product, Equals, AnyRef, Any

Instance Constructors

new Insights(derivedFeatureName: String, stagesApplied: Seq[String], derivedFeatureGroup: Option[String], derivedFeatureValue: Option[String], excluded: Option[Boolean] = None, corr: Option[Double] = None, cramersV: Option[Double] = None, mutualInformation: Option[Double] = None, pointwiseMutualInformation: Map[String, Double] = Map.empty, countMatrix: Map[String, Double] = Map.empty, contribution: Seq[Double] = Seq.empty, min: Option[Double] = None, max: Option[Double] = None, mean: Option[Double] = None, variance: Option[Double] = None)

derivedFeatureName
name of derived feature
stagesApplied
the stageNames of all stages applied to make feature from the raw input feature
derivedFeatureGroup
grouping of this feature if the feature is a pivot
derivedFeatureValue
value of the feature if the feature is a numeric encoding of a non-numeric feature or bucket
excluded
was this derived feature excluded from the model by the sanity checker
corr
the correlation of this feature with the label
cramersV
the cramersV of this feature with the label (when both label and feature are categorical)
mutualInformation
the mutual information for this feature (and all features in its grouping) with the label (categorical features only)
pointwiseMutualInformation
the mutual information of this feature with each value of the label (categorical features only)
countMatrix
the counts of the occurrence of this feature with each of the label values (categorical features only)
contribution
the contribution of this feature to the model (eg feature importance for random forest, weight for logistic regression)
min
the min value of this feature
max
the max value of this feature
mean
the mean value of this feature
variance
the variance of this feature

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
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val contribution: Seq[Double]

the contribution of this feature to the model (eg feature importance for random forest, weight for logistic regression)
val corr: Option[Double]

the correlation of this feature with the label
val countMatrix: Map[String, Double]

the counts of the occurrence of this feature with each of the label values (categorical features only)
val cramersV: Option[Double]

the cramersV of this feature with the label (when both label and feature are categorical)
val derivedFeatureGroup: Option[String]

grouping of this feature if the feature is a pivot
val derivedFeatureName: String

name of derived feature
val derivedFeatureValue: Option[String]

value of the feature if the feature is a numeric encoding of a non-numeric feature or bucket
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val excluded: Option[Boolean]

was this derived feature excluded from the model by the sanity checker
def finalize(): Unit

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

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val max: Option[Double]

the max value of this feature
val mean: Option[Double]

the mean value of this feature
val min: Option[Double]

the min value of this feature
val mutualInformation: Option[Double]

the mutual information for this feature (and all features in its grouping) with the label (categorical features only)
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val pointwiseMutualInformation: Map[String, Double]

the mutual information of this feature with each value of the label (categorical features only)
val stagesApplied: Seq[String]

the stageNames of all stages applied to make feature from the raw input feature
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val variance: Option[Double]

the variance of this feature
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 Doc: package op

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val contribution: Seq[Double]

val corr: Option[Double]

val countMatrix: Map[String, Double]

val cramersV: Option[Double]

val derivedFeatureGroup: Option[String]

val derivedFeatureName: String

val derivedFeatureValue: Option[String]

final def eq(arg0: AnyRef): Boolean

val excluded: Option[Boolean]

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

val max: Option[Double]

val mean: Option[Double]

val min: Option[Double]

val mutualInformation: Option[Double]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val pointwiseMutualInformation: Map[String, Double]

val stagesApplied: Seq[String]

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

val variance: Option[Double]

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 AnyRef

Inherited from Any

Ungrouped