org.pmml4s.common

HasModelAttributes

trait HasModelAttributes extends AnyRef

Holds common attributes of a PMML model.

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Known Subclasses
AnomalyDetectionAttributes, AnomalyDetectionModel, AssociationAttributes, AssociationModel, ClusteringAttributes, ClusteringModel, DataModel, DecisionTree, EmbeddedModel, GeneralRegressionAttributes, GeneralRegressionModel, HasAnomalyDetectionAttributes, HasAssociationAttributes, HasClusteringAttributes, HasGeneralRegressionAttributes, HasNaiveBayesAttributes, HasNearestNeighborAttributes, HasNeuralNetworkAttributes, HasRegressionAttributes, HasScorecardAttributes, HasSupportVectorMachineAttributes, HasTreeAttributes, HasWrappedAnomalyDetectionAttributes, HasWrappedAssociationAttributes, HasWrappedClusteringAttributes, HasWrappedGeneralRegressionAttributes, HasWrappedModelAttributes, HasWrappedNaiveBayesAttributes, HasWrappedNearestNeighborAttributes, HasWrappedNeuralNetworkAttributes, HasWrappedRegressionAttributes, HasWrappedScorecardAttributes, HasWrappedSupportVectorMachineAttributes, HasWrappedTreeAttributes, MiningModel, Model, ModelAttributes, MutableAnomalyDetectionModel, MutableMiningModel, MutableModel, NaiveBayesAttributes, NaiveBayesModel, NearestNeighborAttributes, NearestNeighborModel, NeuralNetwork, NeuralNetworkAttributes, Regression, RegressionAttributes, RegressionModel, RuleSetModel, Scorecard, ScorecardAttributes, SupportVectorMachineAttributes, SupportVectorMachineModel, TransformationModel, TreeAttributes, TreeModel
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Abstract Value Members

  1. 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.

  2. abstract def functionName: MiningFunction

    Describe the kind of mining model, e.

    Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.

  3. 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.

  4. 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.

Concrete Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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  8. final def eq(arg0: AnyRef): Boolean

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  10. def finalize(): Unit

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  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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  13. def isAssociationRules: Boolean

    Tests if this is a association rules model.

  14. def isClassification: Boolean

    Tests if this is a classification model.

  15. def isClustering: Boolean

    Tests if this is a clustering model.

  16. final def isInstanceOf[T0]: Boolean

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  17. def isMixed: Boolean

    Tests if this is a mixed model.

  18. def isRegression: Boolean

    Tests if this is a regression model.

  19. def isSequences: Boolean

    Tests if this is a sequences model.

  20. def isTimeSeries: Boolean

    Tests if this is a time series model.

  21. final def ne(arg0: AnyRef): Boolean

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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

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  25. def toString(): String

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  26. final def wait(): Unit

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

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

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