class GeneralRegressionAttributes extends ModelAttributes with HasGeneralRegressionAttributes
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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)
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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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- Any
- val baselineStrataVariable: Option[Field]
If modelType is CoxRegression, this variable is optional, if present it is used during scoring (see the description of scoring procedures below).
If modelType is CoxRegression, this variable is optional, if present it is used during scoring (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField containing a categorical variable.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native()
- val cumulativeLink: Option[CumulativeLinkFunction]
Specifies the type of cumulative link function to use when ordinalMultinomial model type is specified.
Specifies the type of cumulative link function to use when ordinalMultinomial model type is specified.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val distParameter: Option[Double]
Specifies an ancillary parameter value for the negative binomial distribution.
Specifies an ancillary parameter value for the negative binomial distribution.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val distribution: Option[Distribution]
The probability distribution of the dependent variable for generalizedLinear model may be specified as normal, binomial, gamma, inverse Gaussian, negative binomial, or Poisson.
The probability distribution of the dependent variable for generalizedLinear model may be specified as normal, binomial, gamma, inverse Gaussian, negative binomial, or Poisson. Note that binomial distribution can be used in two situations: either the target is categorical with two categories or a trialsVariable or trialsValue is specified.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val endTimeVariable: Option[Field]
If modelType is CoxRegression, this variable is required during scoring (see the description of scoring procedures below).
If modelType is CoxRegression, this variable is required during scoring (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField containing a continuous variable.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- final def eq(arg0: AnyRef): Boolean
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- def finalize(): Unit
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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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- def hashCode(): Int
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- 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.
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- HasModelAttributes
- def isClustering: Boolean
Tests if this is a clustering model.
Tests if this is a clustering model.
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- 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]
Specifies the type of link function to use when generalizedLinear model type is specified.
Specifies the type of link function to use when generalizedLinear model type is specified.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val linkParameter: Option[Double]
Specifies an additional number the following link functions need: oddspower and power.
Specifies an additional number the following link functions need: oddspower and power.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val modelDF: Option[Double]
The value of degrees of freedom for the model.
The value of degrees of freedom for the model. This value is needed for computing confidence intervals for predicted values.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- 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
Specifies the type of regression model in use.
Specifies the type of regression model in use.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- final def notifyAll(): Unit
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- val offsetValue: Option[Double]
If present, this value is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models.
If present, this value is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models. It works like a user-specified intercept (see the description of the scoring procedures below). At most one of the attributes offsetVariable and offsetValue can be present in a model.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val offsetVariable: Option[Field]
If present, this variable is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models (see the description of scoring procedures below).
If present, this variable is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val startTimeVariable: Option[Field]
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building.
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building. This attribute must refer to a DataField or a DerivedField containing a continuous variable.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val statusVariable: Option[Field]
If modelType is CoxRegression, this variable is optional.
If modelType is CoxRegression, this variable is optional. This attribute must refer to a DataField or a DerivedField.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val subjectIDVariable: Option[Field]
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building.
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building. This attribute must refer to a DataField or a DerivedField. Explicitly listing all categories of this variable is not recommended.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- final def synchronized[T0](arg0: => T0): T0
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- val targetReferenceCategory: Option[String]
Used for specifying the reference category of the target variable in a multinomial classification model.
Used for specifying the reference category of the target variable in a multinomial classification model. Normally the reference category is the one from DataDictionary that does not appear in the ParamMatrix, but when several models are combined in one PMML file an explicit specification is needed.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val targetVariableName: Option[String]
Name of the target variable (also called response variable).
Name of the target variable (also called response variable). This attribute has been deprecated since PMML 3.0. If present, it should match the name of the target MiningField.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- def toString(): String
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- val trialsValue: Option[Int]
A positive integer used during scoring some generalizedLinear models (see the description of scoring procedure below).
A positive integer used during scoring some generalizedLinear models (see the description of scoring procedure below). At most one of the attributes trialsVariable and trialsValue can be present in a model. This attribute can only be used when the distribution is binomial.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- val trialsVariable: Option[Field]
Specifies an additional variable used during scoring some generalizedLinear models (see the description of scoring procedure below).
Specifies an additional variable used during scoring some generalizedLinear models (see the description of scoring procedure below). This attribute must refer to a DataField or a DerivedField. This attribute can only be used when the distribution is binomial.
- Definition Classes
- GeneralRegressionAttributes → HasGeneralRegressionAttributes
- final def wait(): Unit
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- final def wait(arg0: Long, arg1: Int): Unit
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