Packages

class SVMModel extends ProbabilisticClassificationModel[Vector, SVMModel] with SVMBase

Linear Supertypes
SVMBase, ProbabilisticClassificationModel[Vector, SVMModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, SVMModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, SVMModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[SVMModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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  2. By Inheritance
Inherited
  1. SVMModel
  2. SVMBase
  3. ProbabilisticClassificationModel
  4. ProbabilisticClassifierParams
  5. HasThresholds
  6. HasProbabilityCol
  7. ClassificationModel
  8. ClassifierParams
  9. HasRawPredictionCol
  10. PredictionModel
  11. PredictorParams
  12. HasPredictionCol
  13. HasFeaturesCol
  14. HasLabelCol
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new SVMModel(model: mllib.classification.SVMModel)
  2. new SVMModel(uid: String, model: mllib.classification.SVMModel)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): SVMModel.this.type
    Definition Classes
    Params
  7. def clearThreshold(): SVMModel.this.type
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. def copy(extra: ParamMap): SVMModel
    Definition Classes
    SVMModel → Model → Transformer → PipelineStage → Params
  10. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  11. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  15. def explainParams(): String
    Definition Classes
    Params
  16. def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]
    Attributes
    protected
    Definition Classes
    ClassifierParams
  17. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  18. def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  19. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  20. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  21. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  22. def featuresDataType: DataType
    Attributes
    protected
    Definition Classes
    PredictionModel
  23. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. final val fitIntercept: BooleanParam

    Param for whether to fit the intercept.

    Param for whether to fit the intercept.

    Definition Classes
    SVMBase
  25. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  26. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  27. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  28. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  29. final def getFitIntercept: Boolean

    Definition Classes
    SVMBase
  30. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  31. final def getMiniBatchFraction: Double

    Definition Classes
    SVMBase
  32. final def getNumIterations: Int

    Definition Classes
    SVMBase
  33. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  34. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  35. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  36. final def getProbabilityCol: String
    Definition Classes
    HasProbabilityCol
  37. final def getRawPredictionCol: String
    Definition Classes
    HasRawPredictionCol
  38. final def getRegParam: Double

    Definition Classes
    SVMBase
  39. final def getStepSize: Double

    Definition Classes
    SVMBase
  40. final def getThreshold: Double

    Definition Classes
    SVMBase
  41. def getThresholds: Array[Double]
    Definition Classes
    HasThresholds
  42. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  43. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  44. def hasParent: Boolean
    Definition Classes
    Model
  45. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  46. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  47. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  48. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  49. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  50. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  51. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  52. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  53. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  54. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  61. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def margin(features: Vector): Double
  66. final val miniBatchFraction: DoubleParam

    Param for number of iterations.

    Param for number of iterations.

    Definition Classes
    SVMBase
  67. val model: mllib.classification.SVMModel
  68. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  69. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  70. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  71. def numClasses: Int
    Definition Classes
    SVMModel → ClassificationModel
  72. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )
  73. final val numIterations: IntParam

    Param for number of iterations.

    Param for number of iterations.

    Definition Classes
    SVMBase
  74. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  75. var parent: Estimator[SVMModel]
    Definition Classes
    Model
  76. def predict(features: Vector): Double
    Definition Classes
    SVMModel → ClassificationModel → PredictionModel
  77. def predictProbability(features: Vector): Vector
    Definition Classes
    ProbabilisticClassificationModel
    Annotations
    @Since( "3.0.0" )
  78. def predictRaw(features: Vector): Vector
    Definition Classes
    SVMModel → ClassificationModel
  79. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  80. def probability2prediction(probability: Vector): Double
    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  81. final val probabilityCol: Param[String]
    Definition Classes
    HasProbabilityCol
  82. def raw2prediction(rawPrediction: Vector): Double
    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel
  83. def raw2probability(rawPrediction: Vector): Vector
    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  84. def raw2probabilityInPlace(rawPrediction: Vector): Vector
    Attributes
    protected
    Definition Classes
    SVMModel → ProbabilisticClassificationModel
  85. final val rawPredictionCol: Param[String]
    Definition Classes
    HasRawPredictionCol
  86. final val regParam: DoubleParam

    Param for number of iterations.

    Param for number of iterations.

    Definition Classes
    SVMBase
  87. final def set(paramPair: ParamPair[_]): SVMModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  88. final def set(param: String, value: Any): SVMModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  89. final def set[T](param: Param[T], value: T): SVMModel.this.type
    Definition Classes
    Params
  90. final def setDefault(paramPairs: ParamPair[_]*): SVMModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  91. final def setDefault[T](param: Param[T], value: T): SVMModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  92. def setFeaturesCol(value: String): SVMModel
    Definition Classes
    PredictionModel
  93. def setParent(parent: Estimator[SVMModel]): SVMModel
    Definition Classes
    Model
  94. def setPredictionCol(value: String): SVMModel
    Definition Classes
    PredictionModel
  95. def setProbabilityCol(value: String): SVMModel
    Definition Classes
    ProbabilisticClassificationModel
  96. def setRawPredictionCol(value: String): SVMModel
    Definition Classes
    ClassificationModel
  97. def setThreshold(value: Double): SVMModel.this.type
  98. def setThresholds(value: Array[Double]): SVMModel
    Definition Classes
    ProbabilisticClassificationModel
  99. final val stepSize: DoubleParam

    Param for step size.

    Param for step size.

    Definition Classes
    SVMBase
  100. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  101. final val threshold: DoubleParam

    Param for threshold.

    Param for threshold.

    Definition Classes
    SVMBase
  102. val thresholds: DoubleArrayParam
    Definition Classes
    HasThresholds
  103. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  104. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
  105. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  106. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  107. final def transformImpl(dataset: Dataset[_]): DataFrame
    Definition Classes
    ClassificationModel → PredictionModel
  108. def transformSchema(schema: StructType): StructType
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
  109. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  110. val uid: String
    Definition Classes
    SVMModel → Identifiable
  111. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  112. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  113. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

Inherited from SVMBase

Inherited from ProbabilisticClassificationModel[Vector, SVMModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, SVMModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, SVMModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[SVMModel]

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

getParam

param

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