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

Model produced by FindBestModel.

Linear Supertypes
SynapseMLLogging, HasBestModel, Wrappable, 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. RWrappable
  6. PythonWrappable
  7. BaseWrappable
  8. ComplexParamsWritable
  9. MLWritable
  10. Model
  11. Transformer
  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 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 explainParam(param: Param[_]): String
    Definition Classes
    Params
  7. def explainParams(): String
    Definition Classes
    Params
  8. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  9. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  10. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  11. 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.

  12. def getBestModel: Transformer

    The best model found during evaluation.

    The best model found during evaluation.

    returns

    The best model.

    Definition Classes
    HasBestModel
  13. 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.

  14. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  15. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  16. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  17. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  18. def getRocCurve: Dataset[_]

    Gets the ROC curve with TPR, FPR.

    Gets the ROC curve with TPR, FPR.

    returns

    The evaluation results.

  19. def getScoredDataset: DataFrame

    Gets the scored dataset.

    Gets the scored dataset.

    returns

    The scored dataset for the best model.

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