Class

com.salesforce.op.stages.impl.classification

OpLogisticRegression

Related Doc: package classification

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class OpLogisticRegression extends OpPredictorWrapper[LogisticRegression, LogisticRegressionModel] with OpLogisticRegressionParams

Wrapper around spark ml logistic regression org.apache.spark.ml.classification.LogisticRegression

Linear Supertypes
OpLogisticRegressionParams, LogisticRegressionParams, HasAggregationDepth, HasThreshold, HasWeightCol, HasStandardization, HasTol, HasFitIntercept, HasMaxIter, HasElasticNetParam, HasRegParam, ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassifierParams, HasRawPredictionCol, PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, OpPredictorWrapper[LogisticRegression, LogisticRegressionModel], SparkWrapperParams[LogisticRegression], OpPipelineStage2[RealNN, OPVector, Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[LogisticRegressionModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpLogisticRegression
  2. OpLogisticRegressionParams
  3. LogisticRegressionParams
  4. HasAggregationDepth
  5. HasThreshold
  6. HasWeightCol
  7. HasStandardization
  8. HasTol
  9. HasFitIntercept
  10. HasMaxIter
  11. HasElasticNetParam
  12. HasRegParam
  13. ProbabilisticClassifierParams
  14. HasThresholds
  15. HasProbabilityCol
  16. ClassifierParams
  17. HasRawPredictionCol
  18. PredictorParams
  19. HasPredictionCol
  20. HasFeaturesCol
  21. HasLabelCol
  22. OpPredictorWrapper
  23. SparkWrapperParams
  24. OpPipelineStage2
  25. HasIn2
  26. HasIn1
  27. OpPipelineStage
  28. OpPipelineStageBase
  29. MLWritable
  30. OpPipelineStageParams
  31. InputParams
  32. Estimator
  33. PipelineStage
  34. Logging
  35. Params
  36. Serializable
  37. Serializable
  38. Identifiable
  39. AnyRef
  40. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new OpLogisticRegression(uid: String = UID[OpLogisticRegression])

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Type Members

  1. final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

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    Input Features type

    Input Features type

    Definition Classes
    OpPipelineStage2OpPipelineStageInputParams
  2. final type OutputFeatures = FeatureLike[Prediction]

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    Definition Classes
    OpPipelineStageOpPipelineStageBase

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. final val aggregationDepth: IntParam

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    Definition Classes
    HasAggregationDepth
  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  7. final def checkInputLength(features: Array[_]): Boolean

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    Checks the input length

    Checks the input length

    features

    input features

    returns

    true is input size as expected, false otherwise

    Definition Classes
    OpPipelineStage2InputParams
  8. def checkSerializable: Try[Unit]

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    Check if the stage is serializable

    Check if the stage is serializable

    returns

    Failure if not serializable

    Definition Classes
    OpPipelineStageBase
  9. def checkThresholdConsistency(): Unit

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

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    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    Note: that the convention in spark is to have the uid be a constructor argument, so that copies will share a uid with the original (developers should follow this convention).

    extra

    new parameters want to add to instance

    returns

    a new instance with the same uid

    Definition Classes
    OpPipelineStageBase → 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. final val elasticNetParam: DoubleParam

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    Definition Classes
    HasElasticNetParam
  16. final def eq(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    Params
  19. def explainParams(): String

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

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

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    Definition Classes
    Params
  22. final val family: Param[String]

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.1.0" )
  23. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  24. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. def fit(dataset: Dataset[_]): OpPredictorWrapperModel[LogisticRegressionModel]

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    Function that fits the binary model

    Function that fits the binary model

    Definition Classes
    OpPredictorWrapper → Estimator
  26. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[LogisticRegressionModel]]

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

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

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  29. final val fitIntercept: BooleanParam

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    Definition Classes
    HasFitIntercept
  30. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  31. final def getAggregationDepth: Int

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    Definition Classes
    HasAggregationDepth
  32. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  33. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  34. final def getElasticNetParam: Double

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    Definition Classes
    HasElasticNetParam
  35. def getFamily: String

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.1.0" )
  36. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  37. final def getFitIntercept: Boolean

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    Definition Classes
    HasFitIntercept
  38. final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

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    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  39. final def getInputFeatures(): Array[OPFeature]

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    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  40. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  41. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  42. def getLowerBoundsOnCoefficients: Matrix

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  43. def getLowerBoundsOnIntercepts: Vector

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  44. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  45. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  46. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  47. def getOutput(): FeatureLike[Prediction]

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    Output features that will be created by this stage

    Output features that will be created by this stage

    returns

    feature of type OutputFeatures

    Definition Classes
    OpPipelineStage2OpPipelineStageBase
  48. final def getOutputFeatureName: String

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    Name of output feature (i.e.

    Name of output feature (i.e. column created by this stage)

    Definition Classes
    OpPipelineStage
  49. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  50. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  51. final def getProbabilityCol: String

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    Definition Classes
    HasProbabilityCol
  52. final def getRawPredictionCol: String

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    Definition Classes
    HasRawPredictionCol
  53. final def getRegParam: Double

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    Definition Classes
    HasRegParam
  54. def getSparkMlStage(): Option[LogisticRegression]

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    Method to access the spark stage being wrapped

    Method to access the spark stage being wrapped

    returns

    Option of spark ml stage

    Definition Classes
    SparkWrapperParams
  55. def getStageSavePath(): Option[String]

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    Gets a save path for wrapped spark stage

    Gets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  56. final def getStandardization: Boolean

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    Definition Classes
    HasStandardization
  57. def getThreshold: Double

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    Definition Classes
    LogisticRegressionParams → HasThreshold
  58. def getThresholds: Array[Double]

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    Definition Classes
    LogisticRegressionParams → HasThresholds
  59. final def getTol: Double

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    Definition Classes
    HasTol
  60. final def getTransientFeature(i: Int): Option[TransientFeature]

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    Gets an input feature at index i

    Gets an input feature at index i

    i

    input index

    returns

    maybe an input feature

    Definition Classes
    InputParams
  61. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  62. def getUpperBoundsOnCoefficients: Matrix

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  63. def getUpperBoundsOnIntercepts: Vector

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  64. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  65. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  67. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  68. final def in1: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn1
  69. final def in2: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn2
  70. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

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    Attributes
    protected
    Definition Classes
    Logging
  72. final def inputAsArray(in: InputFeatures): Array[OPFeature]

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    Function to convert InputFeatures to an Array of FeatureLike

    Function to convert InputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStage2InputParams
  73. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  74. val inputParam2Name: String

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    Definition Classes
    OpPredictorWrapper
  75. final def isDefined(param: Param[_]): Boolean

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

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

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    Definition Classes
    Params
  78. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  79. final val labelCol: Param[String]

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    Definition Classes
    HasLabelCol
  80. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  87. def logName: String

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  92. val lowerBoundsOnCoefficients: Param[Matrix]

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  93. val lowerBoundsOnIntercepts: Param[Vector]

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  94. final val maxIter: IntParam

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    Definition Classes
    HasMaxIter
  95. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  98. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

    Attributes
    protected
    Definition Classes
    OpPipelineStageParams
  99. def onSetInput(): Unit

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpLogisticRegressionOpPipelineStageBase
  100. val operationName: String

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    Short unique name of the operation this stage performs

    Short unique name of the operation this stage performs

    returns

    operation name

    Definition Classes
    OpPredictorWrapperOpPipelineStageBase
  101. final def outputAsArray(out: OutputFeatures): Array[OPFeature]

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    Function to convert OutputFeatures to an Array of FeatureLike

    Function to convert OutputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStageOpPipelineStageBase
  102. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  103. def outputIsResponse: Boolean

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    Should output feature be a response? Yes, if any of the input features are.

    Should output feature be a response? Yes, if any of the input features are.

    returns

    true if the the output feature should be a response

    Definition Classes
    OpPipelineStage
  104. val outputParamName: String

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    Definition Classes
    OpPredictorWrapper
  105. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  106. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol
  107. val predictor: LogisticRegression

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  108. final val probabilityCol: Param[String]

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    Definition Classes
    HasProbabilityCol
  109. final val rawPredictionCol: Param[String]

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    Definition Classes
    HasRawPredictionCol
  110. final val regParam: DoubleParam

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    Definition Classes
    HasRegParam
  111. def save(path: String): Unit

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

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

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

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    Definition Classes
    Params
  115. def setAggregationDepth(value: Int): OpLogisticRegression.this.type

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    Suggested depth for treeAggregate (greater than or equal to 2).

    Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

  116. final def setDefault(paramPairs: ParamPair[_]*): OpLogisticRegression.this.type

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

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    Attributes
    protected
    Definition Classes
    Params
  118. def setElasticNetParam(value: Double): OpLogisticRegression.this.type

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    Set the ElasticNet mixing parameter.

    Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

    Note: Fitting under bound constrained optimization only supports L2 regularization, so throws exception if this param is non-zero value.

  119. def setFamily(value: String): OpLogisticRegression.this.type

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    Sets the value of param family.

    Sets the value of param family. Default is "auto".

  120. def setFitIntercept(value: Boolean): OpLogisticRegression.this.type

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    Whether to fit an intercept term.

    Whether to fit an intercept term. Default is true.

  121. final def setInput(features: InputFeatures): OpLogisticRegression.this.type

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    Input features that will be used by the stage

    Input features that will be used by the stage

    returns

    feature of type InputFeatures

    Definition Classes
    OpPipelineStageBase
  122. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpLogisticRegression.this.type

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    Sets input features

    Sets input features

    S

    feature like type

    features

    array of input features

    returns

    this stage

    Attributes
    protected
    Definition Classes
    InputParams
  123. def setLowerBoundsOnCoefficients(value: Matrix): OpLogisticRegression.this.type

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    Set the lower bounds on coefficients if fitting under bound constrained optimization.

  124. def setLowerBoundsOnIntercepts(value: Vector): OpLogisticRegression.this.type

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    Set the lower bounds on intercepts if fitting under bound constrained optimization.

  125. def setMaxIter(value: Int): OpLogisticRegression.this.type

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    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

  126. final def setMetadata(m: Metadata): OpLogisticRegression.this.type

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    Definition Classes
    OpPipelineStageParams
  127. def setOutputFeatureName(name: String): OpLogisticRegression.this.type

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    Definition Classes
    OpPipelineStage
  128. def setRegParam(value: Double): OpLogisticRegression.this.type

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    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

  129. def setSparkMlStage(stage: Option[LogisticRegression]): OpLogisticRegression.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  130. def setStageSavePath(path: String): OpLogisticRegression.this.type

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    Sets a save path for wrapped spark stage

    Sets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  131. def setStandardization(value: Boolean): OpLogisticRegression.this.type

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    Whether to standardize the training features before fitting the model.

    Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.

  132. def setThreshold(value: Double): OpLogisticRegression.this.type

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    Definition Classes
    OpLogisticRegression → LogisticRegressionParams
  133. def setThresholds(value: Array[Double]): OpLogisticRegression.this.type

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    Definition Classes
    OpLogisticRegression → LogisticRegressionParams
  134. def setTol(value: Double): OpLogisticRegression.this.type

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    Set the convergence tolerance of iterations.

    Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy at the cost of more iterations. Default is 1E-6.

  135. def setUpperBoundsOnCoefficients(value: Matrix): OpLogisticRegression.this.type

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    Set the upper bounds on coefficients if fitting under bound constrained optimization.

  136. def setUpperBoundsOnIntercepts(value: Vector): OpLogisticRegression.this.type

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    Set the upper bounds on intercepts if fitting under bound constrained optimization.

  137. def setWeightCol(value: String): OpLogisticRegression.this.type

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    Sets the value of param weightCol.

    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.

  138. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  139. final val sparkMlStage: SparkStageParam[LogisticRegression]

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    Definition Classes
    SparkWrapperParams
  140. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  141. final def stageName: String

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    Stage unique name consisting of the stage operation name and uid

    Stage unique name consisting of the stage operation name and uid

    returns

    stage name

    Definition Classes
    OpPipelineStageBase
  142. final val standardization: BooleanParam

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    Definition Classes
    HasStandardization
  143. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  144. val threshold: DoubleParam

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    Definition Classes
    HasThreshold
  145. final val thresholds: DoubleArrayParam

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    Definition Classes
    HasThresholds
  146. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  147. final val tol: DoubleParam

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    Definition Classes
    HasTol
  148. final def transformSchema(schema: StructType): StructType

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    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    schema

    schema of the input data frame

    returns

    a new schema with the output features added

    Definition Classes
    OpPipelineStageBase
  149. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  150. implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

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    Definition Classes
    OpPredictorWrapper
  151. implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

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    Definition Classes
    OpPredictorWrapper
  152. implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  153. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  154. val uid: String

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    stage uid

    stage uid

    Definition Classes
    OpPredictorWrapper → Identifiable
  155. val upperBoundsOnCoefficients: Param[Matrix]

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  156. val upperBoundsOnIntercepts: Param[Vector]

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    Definition Classes
    LogisticRegressionParams
    Annotations
    @Since( "2.2.0" )
  157. def usingBoundConstrainedOptimization: Boolean

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    Attributes
    protected
    Definition Classes
    LogisticRegressionParams
  158. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    Attributes
    protected
    Definition Classes
    LogisticRegressionParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  159. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  162. final val weightCol: Param[String]

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    Definition Classes
    HasWeightCol
  163. final def write: MLWriter

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from LogisticRegressionParams

Inherited from HasAggregationDepth

Inherited from HasThreshold

Inherited from HasWeightCol

Inherited from HasStandardization

Inherited from HasTol

Inherited from HasFitIntercept

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[LogisticRegression, LogisticRegressionModel]

Inherited from SparkWrapperParams[LogisticRegression]

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[Prediction]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from Estimator[OpPredictorWrapperModel[LogisticRegressionModel]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

expertSetParam

setParam

Ungrouped