trait HasSupportVectorMachineAttributes extends HasModelAttributes
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- 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 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 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
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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 classificationMethod: SVMClassificationMethod
Defines which method is to be used in case of multi-class classification tasks.
Defines which method is to be used in case of multi-class classification tasks. It can be either OneAgainstAll or OneAgainstOne. This attribute is not required for binary classification or regression.
- def clone(): AnyRef
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- protected[lang]
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- 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.
- 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
- 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
- def maxWins: Boolean
Used for classification models only.
Used for classification models only. It determines if the target category corresponding to the highest value of a Support Vector machine is the winner. Default value is false, meaning the target category with the lowest SVM value wins, consistent with previous PMML versions. This attribute also affects the comparisons with threshold value, see below for details.
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
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- def svmRepresentation: SVMRepresentation
Defines whether the SVM function is defined via support vectors or via the coefficients of the hyperplane for the case of linear kernel functions.
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
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- def threshold: Double
Defines a discrimination boundary to be used in case of binary classification or whenever attribute classificationMethod is defined as OneAgainstOne for multi-class classification tasks.
- def toString(): String
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- final def wait(arg0: Long, arg1: Int): Unit
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- def finalize(): Unit
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