trait GenericClassifierParams extends Params

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  1. GenericClassifierParams
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Abstract Value Members

  1. abstract def copy(extra: ParamMap): Params
    Definition Classes
    Params
  2. abstract val uid: String
    Definition Classes
    Identifiable

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val batchSize: IntParam

    Batch size

  7. final def clear(param: Param[_]): GenericClassifierParams.this.type
    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  10. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

  11. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. val dropout: FloatParam

    Dropout coefficient

  13. val epochsN: IntParam

    Maximum number of epochs to train

  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  17. def explainParams(): String
    Definition Classes
    Params
  18. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  19. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  20. val featureScaling: Param[String]

    Feature scaling method.

    Feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. val fixImbalance: BooleanParam

    Fix the imbalance in the training set by replicating examples of under represented categories

  23. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  24. def getBatchSize: Int

    Batch size

  25. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  26. def getDatasetInfo: String

    get descriptive information about the dataset being used

  27. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  28. def getDropout: Float

    Dropout coefficient

  29. def getFeatureScaling: String

    Get feature scaling method

  30. def getFixImbalance: Boolean

    Fix imbalance in training set

  31. def getLabelColumn: String

    Column with label per each document

  32. def getLearningRate: Float

    Learning Rate

  33. def getMaxEpochs: Int

    Maximum number of epochs to train

  34. def getModelFile: String

    Model file name

  35. def getMultiClass: Boolean

    Gets the model multi class prediction mode

  36. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  37. def getOutputLogsPath: String

    Get output logs path

  38. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  39. def getValidationSplit: Float

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

  40. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  41. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  42. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  43. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  44. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  45. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  46. val labelColumn: Param[String]

    Column with label per each document

  47. val learningRate: FloatParam

    Learning Rate

  48. val modelFile: Param[String]

    Location of file of the model used for classification

  49. val multiClass: BooleanParam

    If multiClass is set, the model will return all the labels with corresponding scores.

    If multiClass is set, the model will return all the labels with corresponding scores. By default, multiClass is false.

  50. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  51. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  52. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  53. val outputLogsPath: Param[String]

    Folder path to save training logs.

    Folder path to save training logs. If no path is specified, the logs won't be stored in disk. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

  54. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  55. final def set(paramPair: ParamPair[_]): GenericClassifierParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  56. final def set(param: String, value: Any): GenericClassifierParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  57. final def set[T](param: Param[T], value: T): GenericClassifierParams.this.type
    Definition Classes
    Params
  58. def setBatchSize(batch: Int): GenericClassifierParams.this.type

    Batch size

  59. def setDatasetInfo(value: String): GenericClassifierParams.this.type

    set descriptive information about the dataset being used

  60. final def setDefault(paramPairs: ParamPair[_]*): GenericClassifierParams.this.type
    Attributes
    protected
    Definition Classes
    Params
  61. final def setDefault[T](param: Param[T], value: T): GenericClassifierParams.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  62. def setDropout(dropout: Float): GenericClassifierParams.this.type

    Dropout coefficient

  63. def setEpochsNumber(epochs: Int): GenericClassifierParams.this.type

    Maximum number of epochs to train

  64. def setFeatureScaling(featureScaling: String): GenericClassifierParams.this.type

    Set the feature scaling method.

    Set the feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

  65. def setFixImbalance(fix: Boolean): GenericClassifierParams.this.type

    Fix imbalance of training set

  66. def setLabelColumn(column: String): GenericClassifierParams.this.type

    Column with label per each document

  67. def setModelFile(modelFile: String): GenericClassifierParams.this.type

    Set the model file name

  68. def setMultiClass(value: Boolean): GenericClassifierParams.this.type

    Sets the model in multi class prediction mode

  69. def setOutputLogsPath(outputLogsPath: String): GenericClassifierParams.this.type

    Set the output log path

  70. def setValidationSplit(validationSplit: Float): GenericClassifierParams.this.type

    Choose the proportion of training dataset to be validated against the model on each Epoch.

    Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

  71. def setlearningRate(lr: Float): GenericClassifierParams.this.type

    Learning Rate

  72. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  73. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  74. val validationSplit: FloatParam

    The proportion of training dataset to be used as validation set.

    The proportion of training dataset to be used as validation set.

    The model will be validated against this dataset on each Epoch and will not be used for training. The value should be between 0.0 and 1.0.

  75. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  76. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  77. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

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getParam

param

setParam

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