trait HasNaiveBayesAttributes 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.
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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.
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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.
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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.
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abstract
def
threshold: Double
Specifies a default (usually very small) probability to use in lieu of P(Ij* | Tk) when count[Ij*Ti] is zero.
Specifies a default (usually very small) probability to use in lieu of P(Ij* | Tk) when count[Ij*Ti] is zero. Similarly, since the probabilily of a continuous distribution can reach the value of 0 as the lower limit, the same threshold parameter is used as the probability of the continuous variable when the calculated probability of the distribution falls below that value.
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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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clone(): AnyRef
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def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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def
getClass(): Class[_]
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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
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def
isClassification: Boolean
Tests if this is a classification model.
Tests if this is a classification model.
- Definition Classes
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def
isClustering: Boolean
Tests if this is a clustering model.
Tests if this is a clustering model.
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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.
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def
isRegression: Boolean
Tests if this is a regression model.
Tests if this is a regression model.
- Definition Classes
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def
isSequences: Boolean
Tests if this is a sequences model.
Tests if this is a sequences model.
- Definition Classes
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def
isTimeSeries: Boolean
Tests if this is a time series model.
Tests if this is a time series model.
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final
def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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def
notifyAll(): Unit
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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