Class/Object

com.johnsnowlabs.nlp.annotators.generic_classifier

GenericClassifierApproach

Related Docs: object GenericClassifierApproach | package generic_classifier

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class GenericClassifierApproach extends AnnotatorApproach[GenericClassifierModel] with Licensed

Linear Supertypes
Licensed, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GenericClassifierApproach
  2. Licensed
  3. AnnotatorApproach
  4. CanBeLazy
  5. DefaultParamsWritable
  6. MLWritable
  7. HasOutputAnnotatorType
  8. HasOutputAnnotationCol
  9. HasInputAnnotationCols
  10. Estimator
  11. PipelineStage
  12. Logging
  13. Params
  14. Serializable
  15. Serializable
  16. Identifiable
  17. AnyRef
  18. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new GenericClassifierApproach()

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  2. new GenericClassifierApproach(uid: String)

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Type Members

  1. type AnnotatorType = String

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    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): GenericClassifierModel

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    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  7. val batchSize: IntParam

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    Batch size

  8. def beforeTraining(spark: SparkSession): Unit

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    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  9. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  10. final def clear(param: Param[_]): GenericClassifierApproach.this.type

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    Definition Classes
    Params
  11. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  12. final def copy(extra: ParamMap): Estimator[GenericClassifierModel]

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    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  13. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  14. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  15. val description: String

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    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  16. val dropout: FloatParam

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    Dropout coefficient

  17. val epochsN: IntParam

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    Maximum number of epochs to train

  18. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  19. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  20. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  21. def explainParams(): String

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    Definition Classes
    Params
  22. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  23. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  24. val featureScaling: Param[String]

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    Feature scaling method.

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

  25. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  26. final def fit(dataset: Dataset[_]): GenericClassifierModel

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    Definition Classes
    AnnotatorApproach → Estimator
  27. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[GenericClassifierModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  28. def fit(dataset: Dataset[_], paramMap: ParamMap): GenericClassifierModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  29. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GenericClassifierModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  30. val fixImbalance: BooleanParam

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

  31. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  32. def getBatchSize: Int

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    Batch size

  33. final def getClass(): Class[_]

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    Definition Classes
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  34. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  35. def getDropout: Float

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    Dropout coefficient

  36. def getFeatureScaling: String

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    Get feature scaling method

  37. def getFixImbalance: Boolean

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    Fix imbalance in training set

  38. def getInputCols: Array[String]

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    Definition Classes
    HasInputAnnotationCols
  39. def getLabelColumn: String

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    Column with label per each document

  40. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy
  41. def getLearningRate: Float

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    Learning Rate

  42. def getMaxEpochs: Int

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    Maximum number of epochs to train

  43. def getModelFile: String

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    Model file name

  44. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  45. final def getOutputCol: String

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    Definition Classes
    HasOutputAnnotationCol
  46. def getOutputLogsPath: String

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    Get output logs path

  47. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  48. def getValidationSplit: Float

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    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.

  49. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  50. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  51. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  52. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  53. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  54. val inputAnnotatorTypes: Array[AnnotatorType]

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    Input annotator type : SENTENCE_EMBEDDINGS

    Input annotator type : SENTENCE_EMBEDDINGS

    Definition Classes
    GenericClassifierApproach → HasInputAnnotationCols
  55. final val inputCols: StringArrayParam

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  56. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params
  57. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  58. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  59. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  60. val labelColumn: Param[String]

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    Column with label per each document

  61. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy
  62. val learningRate: FloatParam

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    Learning Rate

  63. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  64. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  65. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  66. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  67. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  69. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  70. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  71. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  72. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  73. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  74. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  75. val modelFile: Param[String]

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    Location of file of the model used for classification

  76. def msgHelper(schema: StructType): String

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  77. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  78. final def notify(): Unit

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    Definition Classes
    AnyRef
  79. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  80. def onTrained(model: GenericClassifierModel, spark: SparkSession): Unit

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    Definition Classes
    AnnotatorApproach
  81. val outputAnnotatorType: String

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    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    GenericClassifierApproach → HasOutputAnnotatorType
  82. final val outputCol: Param[String]

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    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  83. val outputLogsPath: Param[String]

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    Path to folder to output logs.

    Path to folder to output logs. If no path is specified, no logs are generated

  84. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  85. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  86. final def set(paramPair: ParamPair[_]): GenericClassifierApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params
  87. final def set(param: String, value: Any): GenericClassifierApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params
  88. final def set[T](param: Param[T], value: T): GenericClassifierApproach.this.type

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    Definition Classes
    Params
  89. def setBatchSize(batch: Int): GenericClassifierApproach.this.type

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    Batch size

  90. final def setDefault(paramPairs: ParamPair[_]*): GenericClassifierApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params
  91. final def setDefault[T](param: Param[T], value: T): GenericClassifierApproach.this.type

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    Attributes
    protected
    Definition Classes
    Params
  92. def setDropout(dropout: Float): GenericClassifierApproach.this.type

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    Dropout coefficient

  93. def setEpochsNumber(epochs: Int): GenericClassifierApproach.this.type

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    Maximum number of epochs to train

  94. def setFeatureScaling(featureScaling: String): GenericClassifierApproach.this.type

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    Set the feature scaling method.

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

  95. def setFixImbalance(fix: Boolean): GenericClassifierApproach.this.type

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    Fix imbalance of training set

  96. final def setInputCols(value: String*): GenericClassifierApproach.this.type

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    Definition Classes
    HasInputAnnotationCols
  97. final def setInputCols(value: Array[String]): GenericClassifierApproach.this.type

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    Definition Classes
    HasInputAnnotationCols
  98. def setLabelColumn(column: String): GenericClassifierApproach.this.type

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    Column with label per each document

  99. def setLazyAnnotator(value: Boolean): GenericClassifierApproach.this.type

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    Definition Classes
    CanBeLazy
  100. def setModelFile(modelFile: String): GenericClassifierApproach.this.type

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    Set the model file name

  101. final def setOutputCol(value: String): GenericClassifierApproach.this.type

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    Definition Classes
    HasOutputAnnotationCol
  102. def setOutputLogsPath(outputLogsPath: String): GenericClassifierApproach.this.type

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    Set the output log path

  103. def setValidationSplit(validationSplit: Float): GenericClassifierApproach.this.type

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    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.

  104. def setlearningRate(lr: Float): GenericClassifierApproach.this.type

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    Learning Rate

  105. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  106. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  107. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): GenericClassifierModel

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    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  108. final def transformSchema(schema: StructType): StructType

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    Definition Classes
    AnnotatorApproach → PipelineStage
  109. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  110. val uid: String

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    Definition Classes
    GenericClassifierApproach → Identifiable
  111. def validate(schema: StructType): Boolean

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    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  112. val validationSplit: FloatParam

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    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.

  113. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  115. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  116. def write: MLWriter

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    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from Licensed

Inherited from AnnotatorApproach[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[GenericClassifierModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

anno

getParam

param

setParam

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