AdvancedOptions

blueprintThreshold: an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs
responseCap: defaults to False, if specified used to cap the maximum response of a model
seed: defaults to null, the random seed to be used if specified
weights: the name of the weight column, if specified, otherwise null.
rateTopPctThreshold: - Optional, the percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.
offset: the list of names of the offset columns, if specified, other- wise null.
exposure: the name of the exposure column, if specified, other- wise null.
eventsCount: the name of the event count column, if specified, otherwise null.
smartDownsampled: whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).
majorityDownsamplingRate: the percentage be- tween 0 and 100 of the majority rows that are kept, or null for projects without smart down- sampling
downsampledMinorityRows: the total number of the minority rows available for modeling, or null for projects without smart downsampling
downsampledMajorityRows: the total number of the majority rows available for modeling, or null for projects without smart downsampling
accuracyOptimizedMb: Include additional, longer-running models that will be run by the autopilot and available to run manually
scaleoutModelingMode: Specifies the behavior of Scaleout models for the project. This is one of disabled, repositoryOnly, autopilot
defaultMonotonicIncreasingFeaturelistId: null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.
defaultMonotonicDecreasingFeaturelistId: null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.
onlyIncludeMonotonicBlueprints: boolean (default to False), whether the project only includes blueprints support enforcing monotonic constraints
blendBestModels: optional, defaults to True. Blend best models during Autopilot run.
minSecondaryValidationModelCount: optional, defaults to 0. Compute “All backtest” scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.
scoringCodeOnly: optional, defaults to False. Keep only models that can be converted to scorable java code during Autopilot run.
prepareModelForDeployment: optional, defaults to True. Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
allowedPairwiseInteractionGroups: (array) –) op- tional. For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to “B”, “C”], [“C”, “D” then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.
returns: AdvancedOptions object

Linear Supertypes

Serializable, Serializable, Product, Equals, AnyRef, Any

Instance Constructors

new AdvancedOptions(blueprintThreshold: Option[Int] = None, responseCap: Option[Double] = None, seed: Option[Int] = None, weights: Option[String] = None, rateTopPctThreshold: Option[Float] = None, offset: Option[String] = None, exposure: Option[String] = None, eventsCount: Option[String] = None, smartDownsampled: Option[Boolean] = None, majorityDownsamplingRate: Option[Double] = None, downsampledMinorityRows: Option[Int] = None, downsampledMajorityRows: Option[Int] = None, accuracyOptimizedMb: Option[Boolean] = None, scaleoutModelingMode: Option[Boolean] = None, defaultMonotonicIncreasingFeaturelistId: Option[String] = None, defaultMonotonicDecreasingFeaturelistId: Option[String] = None, onlyIncludeMonotonicBlueprints: Option[Boolean] = None, blendBestModels: Option[Boolean] = None, minSecondaryValidationModelCount: Option[Int] = None, scoringCodeOnly: Option[Boolean] = None, prepareModelForDeployment: Option[Boolean] = None, allowedPairwiseInteractionGroups: Option[Seq[Seq[String]]] = None)

The advancedOptions object specifies more settings for a project
The advancedOptions object specifies more settings for a project
blueprintThreshold
an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs
responseCap
defaults to False, if specified used to cap the maximum response of a model
seed
defaults to null, the random seed to be used if specified
weights
the name of the weight column, if specified, otherwise null.
rateTopPctThreshold
- Optional, the percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.
offset
the list of names of the offset columns, if specified, other- wise null.
exposure
the name of the exposure column, if specified, other- wise null.
eventsCount
the name of the event count column, if specified, otherwise null.
smartDownsampled
whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).
majorityDownsamplingRate
the percentage be- tween 0 and 100 of the majority rows that are kept, or null for projects without smart down- sampling
downsampledMinorityRows
the total number of the minority rows available for modeling, or null for projects without smart downsampling
downsampledMajorityRows
the total number of the majority rows available for modeling, or null for projects without smart downsampling
accuracyOptimizedMb
Include additional, longer-running models that will be run by the autopilot and available to run manually
scaleoutModelingMode
Specifies the behavior of Scaleout models for the project. This is one of disabled, repositoryOnly, autopilot
defaultMonotonicIncreasingFeaturelistId
null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.
defaultMonotonicDecreasingFeaturelistId
null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.
onlyIncludeMonotonicBlueprints
boolean (default to False), whether the project only includes blueprints support enforcing monotonic constraints
blendBestModels
optional, defaults to True. Blend best models during Autopilot run.
minSecondaryValidationModelCount
optional, defaults to 0. Compute “All backtest” scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.
scoringCodeOnly
optional, defaults to False. Keep only models that can be converted to scorable java code during Autopilot run.
prepareModelForDeployment
optional, defaults to True. Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
allowedPairwiseInteractionGroups
(array) –) op- tional. For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to “B”, “C”], [“C”, “D” then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.

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
val accuracyOptimizedMb: Option[Boolean]

Include additional, longer-running models that will be run by the autopilot and available to run manually
val allowedPairwiseInteractionGroups: Option[Seq[Seq[String]]]

(array) –) op- tional.
(array) –) op- tional. For GAM models - specify groups of columns for which pairwise interactions will be allowed. E.g. if set to “B”, “C”], [“C”, “D” then GAM models will allow interactions between columns AxB, BxC, AxC, CxD. All others (AxD, BxD) will not be considered. If not specified - all possible interactions will be considered by model.
final def asInstanceOf[T0]: T0

Definition Classes
Any
val blendBestModels: Option[Boolean]

optional, defaults to True.
optional, defaults to True. Blend best models during Autopilot run.
val blueprintThreshold: Option[Int]

an upper bound on running time (in hours), such that models exceeding the bound will be excluded in subsequent autopilot runs
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@HotSpotIntrinsicCandidate() @throws( ... )
val defaultMonotonicDecreasingFeaturelistId: Option[String]

null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target.
null or str, the ID of the featurelist specifying a set of features with a monotonically decreasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.
val defaultMonotonicIncreasingFeaturelistId: Option[String]

null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target.
null or str, the ID of the featurelist specifying a set of features with a monotonically increasing relationship to the target. All blueprints generated in the project use this as their default monotonic constraint, but it can be overriden at model submission time.
val downsampledMajorityRows: Option[Int]

the total number of the majority rows available for modeling, or null for projects without smart downsampling
val downsampledMinorityRows: Option[Int]

the total number of the minority rows available for modeling, or null for projects without smart downsampling
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
val eventsCount: Option[String]

the name of the event count column, if specified, otherwise null.
val exposure: Option[String]

the name of the exposure column, if specified, other- wise null.
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@HotSpotIntrinsicCandidate()
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val majorityDownsamplingRate: Option[Double]

the percentage be- tween 0 and 100 of the majority rows that are kept, or null for projects without smart down- sampling
val minSecondaryValidationModelCount: Option[Int]

optional, defaults to 0.
optional, defaults to 0. Compute “All backtest” scores (datetime models) or cross validation scores for the specified number of highest ranking models on the Leaderboard, if over the Autopilot default.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@HotSpotIntrinsicCandidate()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@HotSpotIntrinsicCandidate()
val offset: Option[String]

the list of names of the offset columns, if specified, other- wise null.
val onlyIncludeMonotonicBlueprints: Option[Boolean]

boolean (default to False), whether the project only includes blueprints support enforcing monotonic constraints
val prepareModelForDeployment: Option[Boolean]

optional, defaults to True.
optional, defaults to True. Prepare model for deployment during Autopilot run. The preparation includes creating reduced feature list models, retraining best model on higher sample size, computing insights and assigning “RECOMMENDED FOR DEPLOYMENT” label.
val rateTopPctThreshold: Option[Float]

- Optional, the percentage threshold between 0.1 and 50 for specifying the Rate@Top% metric.
val responseCap: Option[Double]

defaults to False, if specified used to cap the maximum response of a model
val scaleoutModelingMode: Option[Boolean]

Specifies the behavior of Scaleout models for the project.
Specifies the behavior of Scaleout models for the project. This is one of disabled, repositoryOnly, autopilot
val scoringCodeOnly: Option[Boolean]

optional, defaults to False.
optional, defaults to False. Keep only models that can be converted to scorable java code during Autopilot run.
val seed: Option[Int]

defaults to null, the random seed to be used if specified
val smartDownsampled: Option[Boolean]

whether the project uses smart downsampling to throw away excess rows of the majority class.
whether the project uses smart downsampling to throw away excess rows of the majority class. Smart downsampled projects express all sample percents in terms of percent of minority rows (as opposed to percent of all rows).
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AdvancedOptions → AnyRef → Any
final def wait(arg0: Long, arg1: Int): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )
val weights: Option[String]

the name of the weight column, if specified, otherwise null.

Deprecated Value Members

def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@Deprecated @deprecated @throws( classOf[java.lang.Throwable] )
Deprecated
(Since version ) see corresponding Javadoc for more information.

Related Doc: package datarobot

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val accuracyOptimizedMb: Option[Boolean]

val allowedPairwiseInteractionGroups: Option[Seq[Seq[String]]]

final def asInstanceOf[T0]: T0

val blendBestModels: Option[Boolean]

val blueprintThreshold: Option[Int]

def clone(): AnyRef

val defaultMonotonicDecreasingFeaturelistId: Option[String]

val defaultMonotonicIncreasingFeaturelistId: Option[String]

val downsampledMajorityRows: Option[Int]

val downsampledMinorityRows: Option[Int]

final def eq(arg0: AnyRef): Boolean

val eventsCount: Option[String]

val exposure: Option[String]

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

val majorityDownsamplingRate: Option[Double]

val minSecondaryValidationModelCount: Option[Int]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val offset: Option[String]

val onlyIncludeMonotonicBlueprints: Option[Boolean]

val prepareModelForDeployment: Option[Boolean]

val rateTopPctThreshold: Option[Float]

val responseCap: Option[Double]

val scaleoutModelingMode: Option[Boolean]

val scoringCodeOnly: Option[Boolean]

val seed: Option[Int]

val smartDownsampled: Option[Boolean]

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

def toString(): String

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

final def wait(arg0: Long): Unit

final def wait(): Unit

val weights: Option[String]

Deprecated Value Members

def finalize(): Unit

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from AnyRef

Inherited from Any

Ungrouped