Packages

class NaiveBayesModel extends ProbabilisticClassificationModel[Vector, NaiveBayesModel] with NaiveBayesParams with MLWritable

Model produced by NaiveBayes

Annotations
@Since( "1.5.0" )
Source
NaiveBayes.scala
Linear Supertypes
MLWritable, NaiveBayesParams, HasWeightCol, ProbabilisticClassificationModel[Vector, NaiveBayesModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, NaiveBayesModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, NaiveBayesModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[NaiveBayesModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. NaiveBayesModel
  2. MLWritable
  3. NaiveBayesParams
  4. HasWeightCol
  5. ProbabilisticClassificationModel
  6. ProbabilisticClassifierParams
  7. HasThresholds
  8. HasProbabilityCol
  9. ClassificationModel
  10. ClassifierParams
  11. HasRawPredictionCol
  12. PredictionModel
  13. PredictorParams
  14. HasPredictionCol
  15. HasFeaturesCol
  16. HasLabelCol
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
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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

    An alias for getOrDefault().

    An alias for getOrDefault().

    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[_]): NaiveBayesModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. def copy(extra: ParamMap): NaiveBayesModel

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    NaiveBayesModel → Model → Transformer → PipelineStage → Params
    Annotations
    @Since( "1.5.0" )
  9. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

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

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  14. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  15. def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    ClassifierParams
  16. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  17. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  18. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  20. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  21. def featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes
    protected
    Definition Classes
    PredictionModel
  22. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  23. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  24. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  25. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  26. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  27. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  28. final def getModelType: String

    Definition Classes
    NaiveBayesParams
  29. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  30. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  31. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  32. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  33. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  34. final def getSmoothing: Double

    Definition Classes
    NaiveBayesParams
  35. def getThresholds: Array[Double]

    Definition Classes
    HasThresholds
  36. final def getWeightCol: String

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

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  39. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  40. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  41. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  42. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  43. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  44. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  45. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  46. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  47. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  48. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  49. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  50. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  56. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. final val modelType: Param[String]

    The model type which is a string (case-sensitive).

    The model type which is a string (case-sensitive). Supported options: "multinomial", "complement", "bernoulli", "gaussian". (default = multinomial)

    Definition Classes
    NaiveBayesParams
  61. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  62. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  63. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  64. val numClasses: Int

    Number of classes (values which the label can take).

    Number of classes (values which the label can take).

    Definition Classes
    NaiveBayesModel → ClassificationModel
    Annotations
    @Since( "1.5.0" )
  65. val numFeatures: Int

    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes
    NaiveBayesModel → PredictionModel
    Annotations
    @Since( "1.6.0" )
  66. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  67. var parent: Estimator[NaiveBayesModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  68. val pi: Vector
    Annotations
    @Since( "2.0.0" )
  69. def predict(features: Vector): Double

    Predict label for the given features.

    Predict label for the given features. This method is used to implement transform() and output predictionCol.

    This default implementation for classification predicts the index of the maximum value from predictRaw().

    Definition Classes
    ClassificationModel → PredictionModel
  70. def predictProbability(features: Vector): Vector

    Predict the probability of each class given the features.

    Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities

    Definition Classes
    ProbabilisticClassificationModel
    Annotations
    @Since( "3.0.0" )
  71. def predictRaw(features: Vector): Vector

    Raw prediction for each possible label.

    Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

    returns

    vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

    Definition Classes
    NaiveBayesModel → ClassificationModel
    Annotations
    @Since( "3.0.0" )
  72. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  73. def probability2prediction(probability: Vector): Double

    Given a vector of class conditional probabilities, select the predicted label.

    Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.

    returns

    predicted label

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  74. final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes
    HasProbabilityCol
  75. def raw2prediction(rawPrediction: Vector): Double

    Given a vector of raw predictions, select the predicted label.

    Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.

    returns

    predicted label

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel
  76. def raw2probability(rawPrediction: Vector): Vector

    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  77. def raw2probabilityInPlace(rawPrediction: Vector): Vector

    Estimate the probability of each class given the raw prediction, doing the computation in-place.

    Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities (modified input vector)

    Attributes
    protected
    Definition Classes
    NaiveBayesModel → ProbabilisticClassificationModel
  78. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  79. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  83. final def setDefault(paramPairs: ParamPair[_]*): NaiveBayesModel.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

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

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  85. def setFeaturesCol(value: String): NaiveBayesModel

    Definition Classes
    PredictionModel
  86. def setParent(parent: Estimator[NaiveBayesModel]): NaiveBayesModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  87. def setPredictionCol(value: String): NaiveBayesModel

    Definition Classes
    PredictionModel
  88. def setProbabilityCol(value: String): NaiveBayesModel

    Definition Classes
    ProbabilisticClassificationModel
  89. def setRawPredictionCol(value: String): NaiveBayesModel

    Definition Classes
    ClassificationModel
  90. def setThresholds(value: Array[Double]): NaiveBayesModel

    Definition Classes
    ProbabilisticClassificationModel
  91. val sigma: Matrix
    Annotations
    @Since( "3.0.0" )
  92. final val smoothing: DoubleParam

    The smoothing parameter.

    The smoothing parameter. (default = 1.0).

    Definition Classes
    NaiveBayesParams
  93. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  94. val theta: Matrix
    Annotations
    @Since( "2.0.0" )
  95. val thresholds: DoubleArrayParam

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  96. def toString(): String
    Definition Classes
    NaiveBayesModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "1.5.0" )
  97. def transform(dataset: Dataset[_]): DataFrame

    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    dataset

    input dataset

    returns

    transformed dataset

    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
  98. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  99. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  100. final def transformImpl(dataset: Dataset[_]): DataFrame
    Definition Classes
    ClassificationModel → PredictionModel
  101. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
  102. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

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

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    NaiveBayesModel → Identifiable
    Annotations
    @Since( "1.5.0" )
  104. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  105. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  106. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  107. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  108. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  109. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    NaiveBayesModel → MLWritable
    Annotations
    @Since( "1.6.0" )

Inherited from MLWritable

Inherited from NaiveBayesParams

Inherited from HasWeightCol

Inherited from ProbabilisticClassificationModel[Vector, NaiveBayesModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, NaiveBayesModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, NaiveBayesModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[NaiveBayesModel]

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

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters