class FindBestModel extends Estimator[BestModel] with FindBestModelParams with SynapseMLLogging

Evaluates and chooses the best model from a list of models.

Linear Supertypes
SynapseMLLogging, FindBestModelParams, HasEvaluationMetric, ComplexParamsWritable, MLWritable, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, Estimator[BestModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. FindBestModel
  2. SynapseMLLogging
  3. FindBestModelParams
  4. HasEvaluationMetric
  5. ComplexParamsWritable
  6. MLWritable
  7. Wrappable
  8. DotnetWrappable
  9. RWrappable
  10. PythonWrappable
  11. BaseWrappable
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new FindBestModel()
  2. new FindBestModel(uid: String)

Value Members

  1. final def clear(param: Param[_]): FindBestModel.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): FindBestModel
    Definition Classes
    FindBestModel → Estimator → PipelineStage → Params
  3. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  4. val evaluationMetric: Param[String]
    Definition Classes
    HasEvaluationMetric
  5. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  6. def explainParams(): String
    Definition Classes
    Params
  7. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  8. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  9. def fit(dataset: Dataset[_]): BestModel

    dataset

    - The input dataset, to be fitted

    returns

    The Model that results from the fitting

    Definition Classes
    FindBestModel → Estimator
  10. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[BestModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  11. def fit(dataset: Dataset[_], paramMap: ParamMap): BestModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  12. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BestModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  13. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  14. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  15. def getEvaluationMetric: String

    Definition Classes
    HasEvaluationMetric
  16. def getModels: Array[Transformer]

  17. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  18. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  19. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  20. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  21. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  22. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  23. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  24. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  25. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  26. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  27. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  28. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  29. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  30. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  31. val models: TransformerArrayParam

    List of models to be evaluated.

    List of models to be evaluated. The list is an Array of models

  32. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  33. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  34. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  35. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  36. final def set[T](param: Param[T], value: T): FindBestModel.this.type
    Definition Classes
    Params
  37. def setEvaluationMetric(value: String): FindBestModel.this.type

    Definition Classes
    HasEvaluationMetric
  38. def setModels(value: ArrayList[Transformer]): FindBestModel.this.type
  39. def setModels(value: Array[Transformer]): FindBestModel.this.type

  40. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  41. def transformSchema(schema: StructType): StructType
    Definition Classes
    FindBestModel → PipelineStage
  42. val uid: String
    Definition Classes
    FindBestModel → SynapseMLLogging → Identifiable
  43. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable