Packages

class ComputeModelStatistics extends Transformer with ComputeModelStatisticsParams with SynapseMLLogging

Evaluates the given scored dataset.

Linear Supertypes
SynapseMLLogging, ComputeModelStatisticsParams, HasEvaluationMetric, HasScoredLabelsCol, HasScoresCol, HasLabelCol, DefaultParamsWritable, MLWritable, Wrappable, RWrappable, PythonWrappable, BaseWrappable, Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ComputeModelStatistics
  2. SynapseMLLogging
  3. ComputeModelStatisticsParams
  4. HasEvaluationMetric
  5. HasScoredLabelsCol
  6. HasScoresCol
  7. HasLabelCol
  8. DefaultParamsWritable
  9. MLWritable
  10. Wrappable
  11. RWrappable
  12. PythonWrappable
  13. BaseWrappable
  14. Transformer
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

Value Members

  1. final def clear(param: Param[_]): ComputeModelStatistics.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): Transformer
    Definition Classes
    ComputeModelStatistics → Transformer → 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. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  9. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  10. def getEvaluationMetric: String

    Definition Classes
    HasEvaluationMetric
  11. def getLabelCol: String

    Definition Classes
    HasLabelCol
  12. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  13. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  14. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  15. def getScoredLabelsCol: String

    Definition Classes
    HasScoredLabelsCol
  16. def getScoresCol: String

    Definition Classes
    HasScoresCol
  17. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  18. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  19. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  20. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  21. val labelCol: Param[String]

    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol
  22. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  23. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  24. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  25. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  26. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  27. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  28. lazy val metricsLogger: MetricsLogger
  29. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  30. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  31. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  32. var rocCurve: DataFrame

    The ROC curve evaluated for a binary classifier.

  33. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  34. val scoredLabelsCol: Param[String]

    The name of the scored labels column

    The name of the scored labels column

    Definition Classes
    HasScoredLabelsCol
  35. val scoresCol: Param[String]

    The name of the scores column

    The name of the scores column

    Definition Classes
    HasScoresCol
  36. final def set[T](param: Param[T], value: T): ComputeModelStatistics.this.type
    Definition Classes
    Params
  37. def setEvaluationMetric(value: String): ComputeModelStatistics.this.type

    Definition Classes
    HasEvaluationMetric
  38. def setLabelCol(value: String): ComputeModelStatistics.this.type

    Definition Classes
    HasLabelCol
  39. def setScoredLabelsCol(value: String): ComputeModelStatistics.this.type

    Definition Classes
    HasScoredLabelsCol
  40. def setScoresCol(value: String): ComputeModelStatistics.this.type

    Definition Classes
    HasScoresCol
  41. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  42. def transform(dataset: Dataset[_]): DataFrame

    Calculates the metrics for the given dataset and model.

    Calculates the metrics for the given dataset and model.

    dataset

    the dataset to calculate the metrics for

    returns

    DataFrame whose columns contain the calculated metrics

    Definition Classes
    ComputeModelStatistics → Transformer
  43. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  44. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  45. def transformSchema(schema: StructType): StructType
    Definition Classes
    ComputeModelStatistics → PipelineStage
  46. val uid: String
    Definition Classes
    ComputeModelStatistics → SynapseMLLogging → Identifiable
  47. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable