HasWrappedAnomalyDetectionAttributes

abstract def attributes: AnomalyDetectionAttributes

Common attributes of this model

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def algorithmName: Option[String]

The algorithm name is free-type and can be any description for the specific algorithm that produced the model.

def algorithmType: AlgorithmType

Defines a model type used by the anomaly model.

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def functionName: MiningFunction

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

final def getClass(): Class[_]

def hashCode(): Int

def isAssociationRules: Boolean

Tests if this is a association rules model.

def isClassification: Boolean

Tests if this is a classification model.

def isClustering: Boolean

Tests if this is a clustering model.

final def isInstanceOf[T0]: Boolean

def isMixed: Boolean

Tests if this is a mixed model.

def isRegression: Boolean

Tests if this is a regression model.

def isScorable: Boolean

Indicates if the model is valid for scoring.

def isSequences: Boolean

Tests if this is a sequences model.

def isTimeSeries: Boolean

Tests if this is a time series model.

def modelName: Option[String]

Identifies the model with a unique name in the context of the PMML file.

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def sampleDataSize: Option[Long]

A required parameter for isolation forest models.

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