class GenericLogRegClassifierModel extends GenericClassifierModel with ParamsAndFeaturesWritable

Logistic regression classification

Please check out the Models Hub for available models.

Linear Supertypes
GenericClassifierModel, CheckLicense, HasSafeAnnotate[GenericClassifierModel], HandleExceptionParams, HasSimpleAnnotate[GenericClassifierModel], WriteTensorflowModel, HasStorageRef, GenericClassifierParams, AnnotatorModel[GenericClassifierModel], CanBeLazy, RawAnnotator[GenericClassifierModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[GenericClassifierModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
FinanceFewShotClassifierModel, LegalFewShotClassifierModel, FewShotClassifierModel
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Inherited
  1. GenericLogRegClassifierModel
  2. GenericClassifierModel
  3. CheckLicense
  4. HasSafeAnnotate
  5. HandleExceptionParams
  6. HasSimpleAnnotate
  7. WriteTensorflowModel
  8. HasStorageRef
  9. GenericClassifierParams
  10. AnnotatorModel
  11. CanBeLazy
  12. RawAnnotator
  13. HasOutputAnnotationCol
  14. HasInputAnnotationCols
  15. HasOutputAnnotatorType
  16. ParamsAndFeaturesWritable
  17. HasFeatures
  18. DefaultParamsWritable
  19. MLWritable
  20. Model
  21. Transformer
  22. PipelineStage
  23. Logging
  24. Params
  25. Serializable
  26. Serializable
  27. Identifiable
  28. AnyRef
  29. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    a unique identifier for the instantiated AnnotatorModel

Type Members

  1. type AnnotationContent = Seq[Row]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

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. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

    takes a document and annotations and produces new annotations of this annotator's annotation type

    takes a document and annotations and produces new annotations of this annotator's annotation type

    annotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    GenericLogRegClassifierModel → GenericClassifierModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. val batchSize: IntParam

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    GenericClassifierModel → AnnotatorModel
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
    Definition Classes
    CheckLicense
  17. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  18. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  19. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  20. val classes: StringArrayParam
    Definition Classes
    GenericClassifierModel
  21. final def clear(param: Param[_]): GenericLogRegClassifierModel.this.type
    Definition Classes
    Params
  22. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  23. def copy(extra: ParamMap): GenericClassifierModel
    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  24. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  26. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

    Descriptive information about the dataset being used.

    Definition Classes
    GenericClassifierParams
  27. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  28. def dfAnnotate: UserDefinedFunction
    Definition Classes
    HasSimpleAnnotate
  29. val doExceptionHandling: BooleanParam

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  30. val dropout: FloatParam

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  31. val epochsN: IntParam

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  32. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  34. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  35. def explainParams(): String
    Definition Classes
    Params
  36. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  37. def extraValidateMsg: String
    Attributes
    protected
    Definition Classes
    RawAnnotator
  38. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  39. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  40. val featureScaling: Param[String]

    Feature scaling method.

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

    Definition Classes
    GenericClassifierParams
  41. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  42. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  43. val fixImbalance: BooleanParam

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

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

    Definition Classes
    GenericClassifierParams
  44. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  45. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  46. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  47. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  48. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  49. def getBatchSize: Int

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  50. def getCategories(): Array[String]
    Definition Classes
    GenericClassifierModel
  51. def getCategoryName(id: Int): String
    Definition Classes
    GenericClassifierModel
  52. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  53. def getDatasetInfo: String

    get descriptive information about the dataset being used

    get descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  54. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  55. def getDropout: Float

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  56. def getEncoder: GenericClassifierDataEncoder
    Definition Classes
    GenericClassifierModel
  57. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierParams
  58. def getFixImbalance: Boolean

    Fix imbalance in training set

    Fix imbalance in training set

    Definition Classes
    GenericClassifierParams
  59. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  60. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  61. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  62. def getLearningRate: Float

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  63. def getMaxEpochs: Int

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  64. def getModelFile: String

    Model file name

    Model file name

    Definition Classes
    GenericClassifierParams
  65. def getMultiClass: Boolean

    Gets the model multi class prediction mode

    Gets the model multi class prediction mode

    Definition Classes
    GenericClassifierParams
  66. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  67. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  68. def getOutputLogsPath: String

    Get output logs path

    Get output logs path

    Definition Classes
    GenericClassifierParams
  69. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  70. def getStorageRef: String
    Definition Classes
    HasStorageRef
  71. 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.

    Definition Classes
    GenericClassifierParams
  72. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  73. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  74. def hasParent: Boolean
    Definition Classes
    Model
  75. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  76. val inExceptionMode: Boolean
    Attributes
    protected
    Definition Classes
    HasSafeAnnotate
  77. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  78. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator types : FEATURE_VECTOR

    Input annotator types : FEATURE_VECTOR

    Definition Classes
    GenericLogRegClassifierModel → GenericClassifierModel → HasInputAnnotationCols
  80. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  81. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  82. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  83. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  84. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  85. val labelColumn: Param[String]

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  86. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  87. val learningRate: FloatParam

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  88. def loadModel(sparkSession: SparkSession, tfModel: TensorflowWrapper, categories: Array[String], encoder: GenericClassifierDataEncoder): GenericLogRegClassifierModel.this.type
  89. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  90. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  96. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  97. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  98. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  99. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  100. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  101. def model: TensorflowGenericClassifier
    Definition Classes
    GenericClassifierModel
  102. val modelFile: Param[String]

    Location of file of the model used for classification

    Location of file of the model used for classification

    Definition Classes
    GenericClassifierParams
  103. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  104. 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.

    Definition Classes
    GenericClassifierParams
  105. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  106. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  107. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  108. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    GenericClassifierModel → ParamsAndFeaturesWritable
  109. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  110. val outputAnnotatorType: String

    Output annotator types : CATEGORY

    Output annotator types : CATEGORY

    Definition Classes
    GenericLogRegClassifierModel → GenericClassifierModel → HasOutputAnnotatorType
  111. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  112. 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).

    Definition Classes
    GenericClassifierParams
  113. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  114. var parent: Estimator[GenericClassifierModel]
    Definition Classes
    Model
  115. def safeAnnotate(annotations: Seq[Annotation]): Seq[Annotation]

    A protected method designed to safely annotate a sequence of Annotation objects by handling exceptions.

    A protected method designed to safely annotate a sequence of Annotation objects by handling exceptions.

    annotations

    A sequence of Annotation.

    returns

    A sequence of Annotation objects after processing, potentially containing error annotations.

    Attributes
    protected
    Definition Classes
    HasSafeAnnotate
  116. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  117. def scaleFeatures(features: Array[Array[Float]]): Array[Array[Float]]
    Attributes
    protected
    Definition Classes
    GenericClassifierModel
  118. def set[T](feature: StructFeature[T], value: T): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  119. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  120. def set[T](feature: SetFeature[T], value: Set[T]): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  121. def set[T](feature: ArrayFeature[T], value: Array[T]): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  122. final def set(paramPair: ParamPair[_]): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  123. final def set(param: String, value: Any): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  124. final def set[T](param: Param[T], value: T): GenericLogRegClassifierModel.this.type
    Definition Classes
    Params
  125. def setBatchSize(batch: Int): GenericLogRegClassifierModel.this.type

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  126. def setCategoryNames(categoryNames: Array[String]): GenericLogRegClassifierModel.this.type
    Definition Classes
    GenericClassifierModel
  127. def setDatasetInfo(value: String): GenericLogRegClassifierModel.this.type

    set descriptive information about the dataset being used

    set descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  128. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  129. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  130. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  131. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  132. final def setDefault(paramPairs: ParamPair[_]*): GenericLogRegClassifierModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  133. final def setDefault[T](param: Param[T], value: T): GenericLogRegClassifierModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  134. def setDoExceptionHandling(value: Boolean): GenericLogRegClassifierModel.this.type

    If true, exceptions are handled.

    If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

    Definition Classes
    HandleExceptionParams
  135. def setDropout(dropout: Float): GenericLogRegClassifierModel.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  136. def setEncoder(encoder: GenericClassifierDataEncoder): GenericLogRegClassifierModel.this.type
    Definition Classes
    GenericClassifierModel
  137. def setEpochsNumber(epochs: Int): GenericLogRegClassifierModel.this.type

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  138. def setFeatureScaling(featureScaling: String): GenericLogRegClassifierModel.this.type

    Set the feature scaling method.

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

    Definition Classes
    GenericClassifierParams
  139. def setFixImbalance(fix: Boolean): GenericLogRegClassifierModel.this.type

    Fix imbalance of training set

    Fix imbalance of training set

    Definition Classes
    GenericClassifierParams
  140. final def setInputCols(value: String*): GenericLogRegClassifierModel.this.type
    Definition Classes
    HasInputAnnotationCols
  141. def setInputCols(value: Array[String]): GenericLogRegClassifierModel.this.type
    Definition Classes
    HasInputAnnotationCols
  142. def setLabelColumn(column: String): GenericLogRegClassifierModel.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  143. def setLazyAnnotator(value: Boolean): GenericLogRegClassifierModel.this.type
    Definition Classes
    CanBeLazy
  144. def setModelFile(modelFile: String): GenericLogRegClassifierModel.this.type

    Set the model file name

    Set the model file name

    Definition Classes
    GenericClassifierParams
  145. def setMultiClass(value: Boolean): GenericLogRegClassifierModel.this.type

    Sets the model in multi class prediction mode

    Sets the model in multi class prediction mode

    Definition Classes
    GenericClassifierParams
  146. final def setOutputCol(value: String): GenericLogRegClassifierModel.this.type
    Definition Classes
    HasOutputAnnotationCol
  147. def setOutputLogsPath(outputLogsPath: String): GenericLogRegClassifierModel.this.type

    Set the output log path

    Set the output log path

    Definition Classes
    GenericClassifierParams
  148. def setParent(parent: Estimator[GenericClassifierModel]): GenericClassifierModel
    Definition Classes
    Model
  149. def setStorageRef(value: String): GenericLogRegClassifierModel.this.type
    Definition Classes
    HasStorageRef
  150. def setTensorflowModel(spark: SparkSession, tf: TensorflowWrapper): GenericLogRegClassifierModel.this.type
    Definition Classes
    GenericClassifierModel
  151. def setValidationSplit(validationSplit: Float): GenericLogRegClassifierModel.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.

    Definition Classes
    GenericClassifierParams
  152. def setlearningRate(lr: Float): GenericLogRegClassifierModel.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  153. val storageRef: Param[String]
    Definition Classes
    HasStorageRef
  154. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  155. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  156. final def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    AnnotatorModel → Transformer
  157. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  158. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  159. final def transformSchema(schema: StructType): StructType
    Definition Classes
    RawAnnotator → PipelineStage
  160. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  161. val uid: String
    Definition Classes
    GenericLogRegClassifierModel → GenericClassifierModel → Identifiable
  162. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  163. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  164. 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.

    Definition Classes
    GenericClassifierParams
  165. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  166. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  167. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  168. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  169. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  170. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
    Definition Classes
    WriteTensorflowModel
  171. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
    Definition Classes
    WriteTensorflowModel
  172. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]], savedSignatures: Option[Map[String, String]]): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from GenericClassifierModel

Inherited from CheckLicense

Inherited from HasSafeAnnotate[GenericClassifierModel]

Inherited from HandleExceptionParams

Inherited from HasSimpleAnnotate[GenericClassifierModel]

Inherited from WriteTensorflowModel

Inherited from HasStorageRef

Inherited from GenericClassifierParams

Inherited from AnnotatorModel[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[GenericClassifierModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[GenericClassifierModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters