HasGeneralRegressionAttributes

Abstract Value Members

abstract def 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
HasModelAttributes
abstract def 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.
abstract def cumulativeLink: Option[CumulativeLinkFunction]

Specifies the type of cumulative link function to use when ordinalMultinomial model type is specified.
abstract def distParameter: Option[Double]

Specifies an ancillary parameter value for the negative binomial distribution.
abstract def 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.
abstract def 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.
abstract def 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
HasModelAttributes
abstract def 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
HasModelAttributes
abstract def linkFunction: Option[LinkFunction]

Specifies the type of link function to use when generalizedLinear model type is specified.
abstract def linkParameter: Option[Double]

Specifies an additional number the following link functions need: oddspower and power.
abstract def 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.
abstract def 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
HasModelAttributes
abstract def modelType: GeneralModelType

Specifies the type of regression model in use.
abstract def 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.
abstract def 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.
abstract def 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.
abstract def 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.
abstract def 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.
abstract def 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.
abstract def 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.

Annotations
@PmmlDeprecated( "3.0" )
abstract def 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.
abstract def 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.

Concrete 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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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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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.

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

Definition Classes
Any
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
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
final def ne(arg0: AnyRef): Boolean

Definition Classes
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final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

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

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def toString(): String

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final def wait(): Unit

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

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Related Doc: package model

trait HasGeneralRegressionAttributes extends HasModelAttributes

Abstract Value Members

abstract def algorithmName: Option[String]

abstract def baselineStrataVariable: Option[Field]

abstract def cumulativeLink: Option[CumulativeLinkFunction]

abstract def distParameter: Option[Double]

abstract def distribution: Option[Distribution]

abstract def endTimeVariable: Option[Field]

abstract def functionName: MiningFunction

abstract def isScorable: Boolean

abstract def linkFunction: Option[LinkFunction]

abstract def linkParameter: Option[Double]

abstract def modelDF: Option[Double]

abstract def modelName: Option[String]

abstract def modelType: GeneralModelType

abstract def offsetValue: Option[Double]

abstract def offsetVariable: Option[Field]

abstract def startTimeVariable: Option[Field]

abstract def statusVariable: Option[Field]

abstract def subjectIDVariable: Option[Field]

abstract def targetReferenceCategory: Option[String]

abstract def targetVariableName: Option[String]

abstract def trialsValue: Option[Int]

abstract def trialsVariable: Option[Field]

Concrete Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def isAssociationRules: Boolean

def isClassification: Boolean

def isClustering: Boolean

final def isInstanceOf[T0]: Boolean

def isMixed: Boolean

def isRegression: Boolean

def isSequences: Boolean

def isTimeSeries: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from HasModelAttributes

Inherited from AnyRef

Inherited from Any

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