class NaiveBayesModel extends ProbabilisticClassificationModel[Vector, NaiveBayesModel] with NaiveBayesParams with MLWritable
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- NaiveBayesModel
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- NaiveBayesParams
- HasWeightCol
- ProbabilisticClassificationModel
- ProbabilisticClassifierParams
- HasThresholds
- HasProbabilityCol
- ClassificationModel
- ClassifierParams
- HasRawPredictionCol
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
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- final def $[T](param: Param[T]): T
An alias for
getOrDefault().An alias for
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- final def ==(arg0: Any): Boolean
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- def MDC(key: LogKey, value: Any): MDC
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- final def asInstanceOf[T0]: T0
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- Any
- 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
- def clone(): AnyRef
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- protected[lang]
- Definition Classes
- AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- 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")
- 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 toparamMap. 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
- 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
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- Params
- final def eq(arg0: AnyRef): Boolean
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- AnyRef
- def equals(arg0: AnyRef): Boolean
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- AnyRef → Any
- 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
- def explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam().- Definition Classes
- Params
- final def extractParamMap(): ParamMap
extractParamMapwith no extra values.extractParamMapwith no extra values.- Definition Classes
- Params
- 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
- final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
- 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
- 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
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- 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
- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- final def getLabelCol: String
- Definition Classes
- HasLabelCol
- final def getModelType: String
- Definition Classes
- NaiveBayesParams
- 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
- def getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
- final def getPredictionCol: String
- Definition Classes
- HasPredictionCol
- final def getProbabilityCol: String
- Definition Classes
- HasProbabilityCol
- final def getRawPredictionCol: String
- Definition Classes
- HasRawPredictionCol
- final def getSmoothing: Double
- Definition Classes
- NaiveBayesParams
- def getThresholds: Array[Double]
- Definition Classes
- HasThresholds
- final def getWeightCol: String
- Definition Classes
- HasWeightCol
- 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
- 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
- def hasParent: Boolean
Indicates whether this Model has a corresponding parent.
- def hashCode(): Int
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- AnyRef → Any
- Annotations
- @IntrinsicCandidate() @native()
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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- 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.
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- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
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- Params
- def isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- final val labelCol: Param[String]
Param for label column name.
Param for label column name.
- Definition Classes
- HasLabelCol
- def log: Logger
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- def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
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- def logError(msg: => String, throwable: Throwable): Unit
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- def logError(entry: LogEntry, throwable: Throwable): Unit
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- def logError(entry: LogEntry): Unit
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- def logInfo(msg: => String, throwable: Throwable): Unit
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- def logName: String
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- def logTrace(entry: LogEntry, throwable: Throwable): Unit
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- def logTrace(entry: LogEntry): Unit
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- def logTrace(msg: => String): Unit
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- def logWarning(msg: => String, throwable: Throwable): Unit
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- def logWarning(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- 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
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @IntrinsicCandidate() @native()
- 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")
- 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")
- 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.
- 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.
- val pi: Vector
- Annotations
- @Since("2.0.0")
- 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
- 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")
- 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")
- final val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
- 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
- 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
- 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
- def raw2probability(rawPrediction: Vector): Vector
Non-in-place version of
raw2probabilityInPlace()Non-in-place version of
raw2probabilityInPlace()- Attributes
- protected
- Definition Classes
- ProbabilisticClassificationModel
- 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
- 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
- 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("If the input path already exists but overwrite is not enabled.")
- 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
- 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
- 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
- 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
- final def setDefault[T](param: Param[T], value: T): NaiveBayesModel.this.type
Sets a default value for a param.
- def setFeaturesCol(value: String): NaiveBayesModel
- Definition Classes
- PredictionModel
- 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
- def setPredictionCol(value: String): NaiveBayesModel
- Definition Classes
- PredictionModel
- def setProbabilityCol(value: String): NaiveBayesModel
- Definition Classes
- ProbabilisticClassificationModel
- def setRawPredictionCol(value: String): NaiveBayesModel
- Definition Classes
- ClassificationModel
- def setThresholds(value: Array[Double]): NaiveBayesModel
- Definition Classes
- ProbabilisticClassificationModel
- val sigma: Matrix
- Annotations
- @Since("3.0.0")
- final val smoothing: DoubleParam
The smoothing parameter.
The smoothing parameter. (default = 1.0).
- Definition Classes
- NaiveBayesParams
- final def synchronized[T0](arg0: => T0): T0
- Definition Classes
- AnyRef
- val theta: Matrix
- Annotations
- @Since("2.0.0")
- 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
- def toString(): String
- Definition Classes
- NaiveBayesModel → Identifiable → AnyRef → Any
- Annotations
- @Since("1.5.0")
- 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:
- predicted labels as predictionCol of type
Double - raw predictions (confidences) as rawPredictionCol of type
Vector - probability of each class as probabilityCol of type
Vector.
- dataset
input dataset
- returns
transformed dataset
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
- predicted labels as predictionCol of type
- 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")
- 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()
- final def transformImpl(dataset: Dataset[_]): DataFrame
- Definition Classes
- ClassificationModel → PredictionModel
- 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
transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
- 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()
- 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")
- 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.,
VectorUDTfor vector features.- returns
output schema
- Attributes
- protected
- Definition Classes
- ProbabilisticClassifierParams → ClassifierParams → PredictorParams
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- 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
- def withLogContext(context: Map[String, String])(body: => Unit): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def write: MLWriter
Returns an
MLWriterinstance for this ML instance.Returns an
MLWriterinstance for this ML instance.- Definition Classes
- NaiveBayesModel → MLWritable
- Annotations
- @Since("1.6.0")
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)
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 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.