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, 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. RWrappable
  9. PythonWrappable
  10. BaseWrappable
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. 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. val evaluationMetric: Param[String]
    Definition Classes
    HasEvaluationMetric
  4. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  5. def explainParams(): String
    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  8. def fit(dataset: Dataset[_]): BestModel

    dataset

    - The input dataset, to be fitted

    returns

    The Model that results from the fitting

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

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

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

    List of models to be evaluated.

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

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

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

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