class GeneralRegressionAttributes extends ModelAttributes
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Instance Constructors
- new GeneralRegressionAttributes(functionName: MiningFunction, modelType: GeneralModelType, targetVariableName: Option[String] = None, targetReferenceCategory: Option[String] = None, cumulativeLink: Option[CumulativeLinkFunction] = None, linkFunction: Option[LinkFunction] = None, linkParameter: Option[Double] = None, trialsVariable: Option[Field] = None, trialsValue: Option[Int] = None, distribution: Option[Distribution] = None, distParameter: Option[Double] = None, offsetVariable: Option[Field] = None, offsetValue: Option[Double] = None, modelDF: Option[Double] = None, endTimeVariable: Option[Field] = None, startTimeVariable: Option[Field] = None, subjectIDVariable: Option[Field] = None, statusVariable: Option[Field] = None, baselineStrataVariable: Option[Field] = None, modelName: Option[String] = None, algorithmName: Option[String] = None, isScorable: Boolean = true)
Value Members
- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
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- val algorithmName: Option[String]
The algorithm name is free-type and can be any description for the specific algorithm that produced the model.
The algorithm name is free-type and can be any description for the specific algorithm that produced the model. This attribute is for information only.
- Definition Classes
- GeneralRegressionAttributes → ModelAttributes → HasModelAttributes
- final def asInstanceOf[T0]: T0
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- val baselineStrataVariable: Option[Field]
- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- val cumulativeLink: Option[CumulativeLinkFunction]
- val distParameter: Option[Double]
- val distribution: Option[Distribution]
- val endTimeVariable: Option[Field]
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
- Definition Classes
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- val functionName: MiningFunction
Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.
Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.
- Definition Classes
- GeneralRegressionAttributes → ModelAttributes → HasModelAttributes
- final def getClass(): Class[_ <: AnyRef]
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- Annotations
- @IntrinsicCandidate() @native()
- def hashCode(): Int
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- @IntrinsicCandidate() @native()
- def isAssociationRules: Boolean
Tests if this is a association rules model.
Tests if this is a association rules model.
- Definition Classes
- HasModelAttributes
- def isClassification: Boolean
Tests if this is a classification model.
Tests if this is a classification model.
- Definition Classes
- HasModelAttributes
- def isClustering: Boolean
Tests if this is a clustering model.
Tests if this is a clustering model.
- Definition Classes
- HasModelAttributes
- final def isInstanceOf[T0]: Boolean
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- def isMixed: Boolean
Tests if this is a mixed model.
Tests if this is a mixed model.
- Definition Classes
- HasModelAttributes
- def isRegression: Boolean
Tests if this is a regression model.
Tests if this is a regression model.
- Definition Classes
- HasModelAttributes
- val isScorable: Boolean
Indicates if the model is valid for scoring.
Indicates if the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results.
- Definition Classes
- GeneralRegressionAttributes → ModelAttributes → HasModelAttributes
- def isSequences: Boolean
Tests if this is a sequences model.
Tests if this is a sequences model.
- Definition Classes
- HasModelAttributes
- def isTimeSeries: Boolean
Tests if this is a time series model.
Tests if this is a time series model.
- Definition Classes
- HasModelAttributes
- val linkFunction: Option[LinkFunction]
- val linkParameter: Option[Double]
- val modelDF: Option[Double]
- val modelName: Option[String]
Identifies the model with a unique name in the context of the PMML file.
Identifies the model with a unique name in the context of the PMML file. This attribute is not required. Consumers of PMML models are free to manage the names of the models at their discretion.
- Definition Classes
- GeneralRegressionAttributes → ModelAttributes → HasModelAttributes
- val modelType: GeneralModelType
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
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- @IntrinsicCandidate() @native()
- val offsetValue: Option[Double]
- val offsetVariable: Option[Field]
- val startTimeVariable: Option[Field]
- val statusVariable: Option[Field]
- val subjectIDVariable: Option[Field]
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- val targetReferenceCategory: Option[String]
- val targetVariableName: Option[String]
- def toString(): String
- Definition Classes
- AnyRef → Any
- val trialsValue: Option[Int]
- val trialsVariable: Option[Field]
- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- final def wait(): Unit
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Deprecated Value Members
- def finalize(): Unit
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)