trait TDigestFIModelParams extends Params with HasFeaturesCol with DefaultParamsWritable
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def
clear(param: Param[_]): TDigestFIModelParams.this.type
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copyValues[T <: Params](to: T, extra: ParamMap): T
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defaultCopy[T <: Params](extra: ParamMap): T
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final
val
deviationMeasure: Param[String]
Function to measure the change resulting from randomizing a feature value.
Function to measure the change resulting from randomizing a feature value. Defaults to "auto" (detects whether model is regression or classification). Options: "auto", "dev-rate" (class), "abs-dev" (reg), "rms-dev" (reg)
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explainParam(param: Param[_]): String
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explainParams(): String
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def
extractParamMap(): ParamMap
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def
extractParamMap(extra: ParamMap): ParamMap
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val
featureNames: StringArrayParam
Names to use for features.
Names to use for features. Defaults to f1, f2, ...
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final
val
featuresCol: Param[String]
Column containing feature vectors.
Column containing feature vectors. Expected type is ML Vector. Defaults to "features"
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- HasFeaturesCol
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finalize(): Unit
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get[T](param: Param[T]): Option[T]
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getClass(): Class[_]
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def
getDefault[T](param: Param[T]): Option[T]
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- final def getDeviationMeasure: String
- final def getFeatureNames: Array[String]
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def
getFeaturesCol: String
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- HasFeaturesCol
- final def getImportanceCol: String
- final def getNameCol: String
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def
getOrDefault[T](param: Param[T]): T
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def
getParam(paramName: String): Param[Any]
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def
hasDefault[T](param: Param[T]): Boolean
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hasParam(paramName: String): Boolean
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def
hashCode(): Int
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val
importanceCol: Param[String]
Column name to use for feature importances.
Column name to use for feature importances. Defaults to "importance"
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isDefined(param: Param[_]): Boolean
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isInstanceOf[T0]: Boolean
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def
isSet(param: Param[_]): Boolean
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val
nameCol: Param[String]
Column name to use for feature names.
Column name to use for feature names. Defaults to "name"
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ne(arg0: AnyRef): Boolean
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notify(): Unit
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notifyAll(): Unit
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lazy val
params: Array[Param[_]]
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def
save(path: String): Unit
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def
set(paramPair: ParamPair[_]): TDigestFIModelParams.this.type
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def
set(param: String, value: Any): TDigestFIModelParams.this.type
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def
set[T](param: Param[T], value: T): TDigestFIModelParams.this.type
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def
setDefault(paramPairs: ParamPair[_]*): TDigestFIModelParams.this.type
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def
setDefault[T](param: Param[T], value: T): TDigestFIModelParams.this.type
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- final def setDeviationMeasure(value: String): TDigestFIModelParams.this.type
- final def setFeatureNames(value: Array[String]): TDigestFIModelParams.this.type
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final
def
setFeaturesCol(value: String): TDigestFIModelParams.this.type
- Definition Classes
- HasFeaturesCol
- final def setImportanceCol(value: String): TDigestFIModelParams.this.type
- final def setNameCol(value: String): TDigestFIModelParams.this.type
- final def setTargetModel(value: AnyRef): TDigestFIModelParams.this.type
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
val
targetModel: Param[AnyRef]
A predictive model to compute variable importances against.
A predictive model to compute variable importances against. No default.
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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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def
wait(arg0: Long): Unit
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def
write: MLWriter
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- DefaultParamsWritable → MLWritable