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com.johnsnowlabs.nlp

HasClassifierActivationProperties

trait HasClassifierActivationProperties extends ParamsAndFeaturesWritable

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Inherited
  1. HasClassifierActivationProperties
  2. ParamsAndFeaturesWritable
  3. HasFeatures
  4. Params
  5. Serializable
  6. Serializable
  7. Identifiable
  8. DefaultParamsWritable
  9. MLWritable
  10. AnyRef
  11. Any
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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. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. val activation: Param[String]

    Whether to enable caching DataFrames or RDDs during the training (Default depends on model).

  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. final def clear(param: Param[_]): HasClassifierActivationProperties.this.type
    Definition Classes
    Params
  12. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  13. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  14. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  15. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  17. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  18. def explainParams(): String
    Definition Classes
    Params
  19. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  20. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  21. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  22. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  24. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  25. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  26. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  27. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  28. def getActivation: String

  29. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  30. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  31. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  32. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  33. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  34. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  35. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  36. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  37. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  38. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  39. val multilabel: BooleanParam

    Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).

    Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class

  40. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  41. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  42. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  43. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  44. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  45. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  46. def set[T](feature: StructFeature[T], value: T): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  47. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  48. def set[T](feature: SetFeature[T], value: Set[T]): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  49. def set[T](feature: ArrayFeature[T], value: Array[T]): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  50. final def set(paramPair: ParamPair[_]): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    Params
  51. final def set(param: String, value: Any): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    Params
  52. final def set[T](param: Param[T], value: T): HasClassifierActivationProperties.this.type
    Definition Classes
    Params
  53. def setActivation(value: String): HasClassifierActivationProperties.this.type

  54. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  55. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  56. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  57. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  58. final def setDefault(paramPairs: ParamPair[_]*): HasClassifierActivationProperties.this.type
    Attributes
    protected
    Definition Classes
    Params
  59. final def setDefault[T](param: Param[T], value: T): HasClassifierActivationProperties.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  60. def setMultilabel(value: Boolean): HasClassifierActivationProperties.this.type

    Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).

    Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class

  61. def setThreshold(threshold: Float): HasClassifierActivationProperties.this.type

    Choose the threshold to determine which logits are considered to be positive or negative.

    Choose the threshold to determine which logits are considered to be positive or negative. (Default: 0.5f). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.

  62. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  63. val threshold: FloatParam

    Choose the threshold to determine which logits are considered to be positive or negative.

    Choose the threshold to determine which logits are considered to be positive or negative. (Default: 0.5f). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.

  64. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  65. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  66. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  67. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  68. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from AnyRef

Inherited from Any

getParam

param

setParam

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