Trait

org.pmml4s.model

HasWrappedGeneralRegressionAttributes

Related Doc: package model

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trait HasWrappedGeneralRegressionAttributes extends HasWrappedModelAttributes with HasGeneralRegressionAttributes

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  1. HasWrappedGeneralRegressionAttributes
  2. HasGeneralRegressionAttributes
  3. HasWrappedModelAttributes
  4. HasModelAttributes
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Abstract Value Members

  1. abstract def attributes: GeneralRegressionAttributes

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    Common attributes of this model

    Common attributes of this model

    Definition Classes
    HasWrappedGeneralRegressionAttributesHasWrappedModelAttributes

Concrete Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def algorithmName: Option[String]

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    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
    HasWrappedModelAttributesHasModelAttributes
  5. final def asInstanceOf[T0]: T0

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  6. def baselineStrataVariable: Option[Field]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  8. def cumulativeLink: Option[CumulativeLinkFunction]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  9. def distParameter: Option[Double]

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    Specifies an ancillary parameter value for the negative binomial distribution.

    Specifies an ancillary parameter value for the negative binomial distribution.

    Definition Classes
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  10. def distribution: Option[Distribution]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  11. def endTimeVariable: Option[Field]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  12. final def eq(arg0: AnyRef): Boolean

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  13. def equals(arg0: Any): Boolean

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  14. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  15. def functionName: MiningFunction

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    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
    HasWrappedModelAttributesHasModelAttributes
  16. final def getClass(): Class[_]

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  17. def hashCode(): Int

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  18. def isAssociationRules: Boolean

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    Tests if this is a association rules model.

    Tests if this is a association rules model.

    Definition Classes
    HasModelAttributes
  19. def isClassification: Boolean

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    Tests if this is a classification model.

    Tests if this is a classification model.

    Definition Classes
    HasModelAttributes
  20. def isClustering: Boolean

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    Tests if this is a clustering model.

    Tests if this is a clustering model.

    Definition Classes
    HasModelAttributes
  21. final def isInstanceOf[T0]: Boolean

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  22. def isMixed: Boolean

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    Tests if this is a mixed model.

    Tests if this is a mixed model.

    Definition Classes
    HasModelAttributes
  23. def isRegression: Boolean

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    Tests if this is a regression model.

    Tests if this is a regression model.

    Definition Classes
    HasModelAttributes
  24. def isScorable: Boolean

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    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
    HasWrappedModelAttributesHasModelAttributes
  25. def isSequences: Boolean

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    Tests if this is a sequences model.

    Tests if this is a sequences model.

    Definition Classes
    HasModelAttributes
  26. def isTimeSeries: Boolean

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    Tests if this is a time series model.

    Tests if this is a time series model.

    Definition Classes
    HasModelAttributes
  27. def linkFunction: Option[LinkFunction]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  28. def linkParameter: Option[Double]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  29. def modelDF: Option[Double]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  30. def modelName: Option[String]

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    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
    HasWrappedModelAttributesHasModelAttributes
  31. def modelType: GeneralModelType

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    Specifies the type of regression model in use.

    Specifies the type of regression model in use.

    Definition Classes
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  32. final def ne(arg0: AnyRef): Boolean

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  33. final def notify(): Unit

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  34. final def notifyAll(): Unit

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  35. def offsetValue: Option[Double]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  36. def offsetVariable: Option[Field]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  37. def startTimeVariable: Option[Field]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  38. def statusVariable: Option[Field]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  39. def subjectIDVariable: Option[Field]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  40. final def synchronized[T0](arg0: ⇒ T0): T0

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  41. def targetReferenceCategory: Option[String]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  42. def targetVariableName: Option[String]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  43. def toString(): String

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  44. def trialsValue: Option[Int]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  45. def trialsVariable: Option[Field]

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    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
    HasWrappedGeneralRegressionAttributesHasGeneralRegressionAttributes
  46. final def wait(): Unit

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

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  48. final def wait(arg0: Long): Unit

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Inherited from HasWrappedModelAttributes

Inherited from HasModelAttributes

Inherited from AnyRef

Inherited from Any

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