org.apache.spark.ml.classification

NaiveBayes

class NaiveBayes extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel] with NaiveBayesParams

:: Experimental :: Naive Bayes Classifiers. It supports both Multinomial NB (http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html) which can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB (http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). The input feature values must be nonnegative.

Annotations
@Experimental()
Linear Supertypes
NaiveBayesParams, ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, NaiveBayes, NaiveBayesModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, NaiveBayes, NaiveBayesModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[NaiveBayesModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. NaiveBayes
  2. NaiveBayesParams
  3. ProbabilisticClassifier
  4. ProbabilisticClassifierParams
  5. HasThresholds
  6. HasProbabilityCol
  7. Classifier
  8. ClassifierParams
  9. HasRawPredictionCol
  10. Predictor
  11. PredictorParams
  12. HasPredictionCol
  13. HasFeaturesCol
  14. HasLabelCol
  15. Estimator
  16. PipelineStage
  17. Logging
  18. Params
  19. Serializable
  20. Serializable
  21. Identifiable
  22. AnyRef
  23. Any
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  1. Public
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Instance Constructors

  1. new NaiveBayes()

  2. new NaiveBayes(uid: String)

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  6. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. final def clear(param: Param[_]): NaiveBayes.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Attributes
    protected
    Definition Classes
    Params
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def copy(extra: ParamMap): NaiveBayes

    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.

    Definition Classes
    NaiveBayesPredictorEstimatorPipelineStageParams
    See also

    defaultCopy()

  11. 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
  12. 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
  13. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  15. 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
  16. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance.

    Definition Classes
    Params
    See also

    explainParam()

  17. def extractLabeledPoints(dataset: DataFrame): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  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.

    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 < user-supplied values < 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 finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. def fit(dataset: DataFrame): NaiveBayesModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  23. def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[NaiveBayesModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
  24. def fit(dataset: DataFrame, paramMap: ParamMap): NaiveBayesModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
  25. def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): NaiveBayesModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @varargs()
  26. 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
  27. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  28. 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
  29. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  30. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  31. final def getModelType: String

    Definition Classes
    NaiveBayesParams
  32. 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
  33. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  34. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  35. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  36. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  37. final def getSmoothing: Double

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

    Definition Classes
    HasThresholds
  39. 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
  40. 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
  41. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  42. 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
  43. final def isInstanceOf[T0]: Boolean

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  45. def isTraceEnabled(): Boolean

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

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  47. def log: Logger

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

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

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  54. def logName: String

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

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

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

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

    Attributes
    protected
    Definition Classes
    Logging
  59. 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" and "bernoulli". (default = multinomial)

    Definition Classes
    NaiveBayesParams
  60. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  63. 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.

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

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

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  65. 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
  66. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.

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

    Definition Classes
    HasRawPredictionCol
  67. final def set(paramPair: ParamPair[_]): NaiveBayes.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  68. final def set(param: String, value: Any): NaiveBayes.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
  69. final def set[T](param: Param[T], value: T): NaiveBayes.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  70. final def setDefault(paramPairs: ParamPair[_]*): NaiveBayes.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
  71. final def setDefault[T](param: Param[T], value: T): NaiveBayes.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
  72. def setFeaturesCol(value: String): NaiveBayes

    Definition Classes
    Predictor
  73. def setLabelCol(value: String): NaiveBayes

    Definition Classes
    Predictor
  74. def setModelType(value: String): NaiveBayes.this.type

    Set the model type using a string (case-sensitive).

    Set the model type using a string (case-sensitive). Supported options: "multinomial" and "bernoulli". Default is "multinomial"

  75. def setPredictionCol(value: String): NaiveBayes

    Definition Classes
    Predictor
  76. def setProbabilityCol(value: String): NaiveBayes

    Definition Classes
    ProbabilisticClassifier
  77. def setRawPredictionCol(value: String): NaiveBayes

    Definition Classes
    Classifier
  78. def setSmoothing(value: Double): NaiveBayes.this.type

    Set the smoothing parameter.

    Set the smoothing parameter. Default is 1.0.

  79. def setThresholds(value: Array[Double]): NaiveBayes

    Definition Classes
    ProbabilisticClassifier
  80. final val smoothing: DoubleParam

    The smoothing parameter.

    The smoothing parameter. (default = 1.0).

    Definition Classes
    NaiveBayesParams
  81. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  82. final 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. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold..

    Definition Classes
    HasThresholds
  83. def toString(): String

    Definition Classes
    Identifiable → AnyRef → Any
  84. def train(dataset: DataFrame): NaiveBayesModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    NaiveBayesPredictor
  85. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    PredictorPipelineStage
  86. 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()
  87. 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
    NaiveBayesIdentifiable
  88. 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., org.apache.spark.mllib.linalg.VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  89. def validateParams(): Unit

    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    Params
  90. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from NaiveBayesParams

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 Estimator[NaiveBayesModel]

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