class NaiveBayesModel extends ProbabilisticClassificationModel[Vector, NaiveBayesModel] with NaiveBayesParams with MLWritable
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- NaiveBayesModel
- MLWritable
- NaiveBayesParams
- HasWeightCol
- ProbabilisticClassificationModel
- ProbabilisticClassifierParams
- HasThresholds
- HasProbabilityCol
- ClassificationModel
- ClassifierParams
- HasRawPredictionCol
- PredictionModel
- PredictorParams
- HasPredictionCol
- HasFeaturesCol
- HasLabelCol
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
- final val featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
- final val labelCol: Param[String]
Param for label column name.
Param for label column name.
- Definition Classes
- HasLabelCol
- 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 val predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
- 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
- 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
- final val smoothing: DoubleParam
The smoothing parameter.
The smoothing parameter. (default = 1.0).
- Definition Classes
- NaiveBayesParams
- 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
- 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
Members
- implicit class LogStringContext extends AnyRef
- Definition Classes
- Logging
- 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 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 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
extractParamMap
with no extra values.extractParamMap
with 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 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 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 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 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.
- 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
- final def isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
- 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")
- 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[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
- 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
- val sigma: Matrix
- Annotations
- @Since("3.0.0")
- val theta: Matrix
- Annotations
- @Since("2.0.0")
- 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
transformSchema
and 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
- 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 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")
Parameter setters
- def setFeaturesCol(value: String): NaiveBayesModel
- Definition Classes
- PredictionModel
- 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
Parameter getters
- final def getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- final def getLabelCol: String
- Definition Classes
- HasLabelCol
- final def getModelType: String
- Definition Classes
- NaiveBayesParams
- 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