class BestModel extends Model[BestModel] with ComplexParamsWritable with Wrappable with HasBestModel with SynapseMLLogging

Model produced by FindBestModel.

Linear Supertypes
SynapseMLLogging, HasBestModel, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, ComplexParamsWritable, MLWritable, Model[BestModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. BestModel
  2. SynapseMLLogging
  3. HasBestModel
  4. Wrappable
  5. DotnetWrappable
  6. RWrappable
  7. PythonWrappable
  8. BaseWrappable
  9. ComplexParamsWritable
  10. MLWritable
  11. Model
  12. Transformer
  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 BestModel()
  2. new BestModel(uid: String)

Value Members

  1. val allModelMetrics: DataFrameParam
  2. val bestModel: TransformerParam
    Definition Classes
    HasBestModel
  3. val bestModelMetrics: DataFrameParam
  4. final def clear(param: Param[_]): BestModel.this.type
    Definition Classes
    Params
  5. def copy(extra: ParamMap): BestModel
    Definition Classes
    BestModel → Model → Transformer → PipelineStage → Params
  6. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  7. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  8. def explainParams(): String
    Definition Classes
    Params
  9. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  10. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  11. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. def getAllModelMetrics: Dataset[_]

    Gets a table of metrics from all models compared from the evaluation comparison.

    Gets a table of metrics from all models compared from the evaluation comparison.

    returns

    The model metrics results from all models.

  13. def getBestModel: Transformer

    The best model found during evaluation.

    The best model found during evaluation.

    returns

    The best model.

    Definition Classes
    HasBestModel
  14. def getBestModelMetrics: Dataset[_]

    Gets all of the best model metrics results from the evaluator.

    Gets all of the best model metrics results from the evaluator.

    returns

    All of the best model metrics results.

  15. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  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. def getRocCurve: Dataset[_]

    Gets the ROC curve with TPR, FPR.

    Gets the ROC curve with TPR, FPR.

    returns

    The evaluation results.

  20. def getScoredDataset: DataFrame

    Gets the scored dataset.

    Gets the scored dataset.

    returns

    The scored dataset for the best model.

  21. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  22. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  23. def hasParent: Boolean
    Definition Classes
    Model
  24. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  25. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  26. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  27. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  28. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  29. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  30. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  31. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  32. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  33. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  34. var parent: Estimator[BestModel]
    Definition Classes
    Model
  35. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  36. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  37. val rocCurve: DataFrameParam
  38. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  39. val scoredDataset: DataFrameParam
  40. final def set[T](param: Param[T], value: T): BestModel.this.type
    Definition Classes
    Params
  41. def setAllModelMetrics(v: DataFrame): BestModel.this.type
  42. def setBestModel(v: Transformer): BestModel.this.type
    Definition Classes
    HasBestModel
  43. def setBestModelMetrics(v: DataFrame): BestModel.this.type
  44. def setParent(parent: Estimator[BestModel]): BestModel
    Definition Classes
    Model
  45. def setRocCurve(v: DataFrame): BestModel.this.type
  46. def setScoredDataset(v: DataFrame): BestModel.this.type
  47. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  48. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    BestModel → Transformer
  49. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  50. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  51. def transformSchema(schema: StructType): StructType
    Definition Classes
    BestModel → PipelineStage
  52. val uid: String
    Definition Classes
    BestModel → SynapseMLLogging → Identifiable
  53. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable