NaiveBayesModel (Spark 3.5.5 JavaDoc) (original) (raw)

• Object

• • org.apache.spark.mllib.classification.NaiveBayesModel

• All Implemented Interfaces:
  java.io.Serializable, ClassificationModel, Saveable

public class NaiveBayesModel
extends Object
implements ClassificationModel, scala.Serializable, Saveable
Model for Naive Bayes Classifiers.
param: labels list of labels param: pi log of class priors, whose dimension is C, number of labels param: theta log of class conditional probabilities, whose dimension is C-by-D, where D is number of features param: modelType The type of NB model to fit can be "multinomial" or "bernoulli"
See Also:
Serialized Form

• • Nested Class Summary

  Nested Classes

  Modifier and Type	Class and Description
  static class	NaiveBayesModel.SaveLoadV1_0$
  static class	NaiveBayesModel.SaveLoadV2_0$

  • Method Summary

  All Methods Static Methods Instance Methods Concrete Methods

  Modifier and Type	Method and Description
  double[]	labels()
  static NaiveBayesModel	load(SparkContext sc, String path)
  String	modelType()
  double[]	pi()
  RDD	predict(RDD<Vector> testData) Predict values for the given data set using the model trained.
  double	predict(Vector testData) Predict values for a single data point using the model trained.
  RDD<Vector>	predictProbabilities(RDD<Vector> testData) Predict values for the given data set using the model trained.
  Vector	predictProbabilities(Vector testData) Predict posterior class probabilities for a single data point using the model trained.
  void	save(SparkContext sc, String path) Save this model to the given path.
  double[][]	theta()

     * ### Methods inherited from class Object  
     `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`  
     * ### Methods inherited from interface org.apache.spark.mllib.classification.[ClassificationModel](../../../../../org/apache/spark/mllib/classification/ClassificationModel.html "interface in org.apache.spark.mllib.classification")  
     `[predict](../../../../../org/apache/spark/mllib/classification/ClassificationModel.html#predict-org.apache.spark.api.java.JavaRDD-)`

• • Method Detail

     * #### load  
     public static [NaiveBayesModel](../../../../../org/apache/spark/mllib/classification/NaiveBayesModel.html "class in org.apache.spark.mllib.classification") load([SparkContext](../../../../../org/apache/spark/SparkContext.html "class in org.apache.spark") sc,  
                                        String path)  
     * #### labels  
     public double[] labels()  
     * #### pi  
     public double[] pi()  
     * #### theta  
     public double[][] theta()  
     * #### modelType  
     public String modelType()  
     * #### predict  
     public [RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<Object> predict([RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<[Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")> testData)  
     Predict values for the given data set using the model trained.  
     Specified by:  
     `[predict](../../../../../org/apache/spark/mllib/classification/ClassificationModel.html#predict-org.apache.spark.rdd.RDD-)` in interface `[ClassificationModel](../../../../../org/apache/spark/mllib/classification/ClassificationModel.html "interface in org.apache.spark.mllib.classification")`  
     Parameters:  
     `testData` \- RDD representing data points to be predicted  
     Returns:  
     an RDD\[Double\] where each entry contains the corresponding prediction  
     * #### predict  
     public double predict([Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg") testData)  
     Predict values for a single data point using the model trained.  
     Specified by:  
     `[predict](../../../../../org/apache/spark/mllib/classification/ClassificationModel.html#predict-org.apache.spark.mllib.linalg.Vector-)` in interface `[ClassificationModel](../../../../../org/apache/spark/mllib/classification/ClassificationModel.html "interface in org.apache.spark.mllib.classification")`  
     Parameters:  
     `testData` \- array representing a single data point  
     Returns:  
     predicted category from the trained model  
     * #### predictProbabilities  
     public [RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<[Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")> predictProbabilities([RDD](../../../../../org/apache/spark/rdd/RDD.html "class in org.apache.spark.rdd")<[Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg")> testData)  
     Predict values for the given data set using the model trained.  
     Parameters:  
     `testData` \- RDD representing data points to be predicted  
     Returns:  
     an RDD\[Vector\] where each entry contains the predicted posterior class probabilities, in the same order as class labels  
     * #### predictProbabilities  
     public [Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg") predictProbabilities([Vector](../../../../../org/apache/spark/mllib/linalg/Vector.html "interface in org.apache.spark.mllib.linalg") testData)  
     Predict posterior class probabilities for a single data point using the model trained.  
     Parameters:  
     `testData` \- array representing a single data point  
     Returns:  
     predicted posterior class probabilities from the trained model, in the same order as class labels  
     * #### save  
     public void save([SparkContext](../../../../../org/apache/spark/SparkContext.html "class in org.apache.spark") sc,  
                      String path)  
     Description copied from interface: `[Saveable](../../../../../org/apache/spark/mllib/util/Saveable.html#save-org.apache.spark.SparkContext-java.lang.String-)`  
     Save this model to the given path.  
      This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/  
      The model may be loaded using `Loader.load`.  
     Specified by:  
     `[save](../../../../../org/apache/spark/mllib/util/Saveable.html#save-org.apache.spark.SparkContext-java.lang.String-)` in interface `[Saveable](../../../../../org/apache/spark/mllib/util/Saveable.html "interface in org.apache.spark.mllib.util")`  
     Parameters:  
     `sc` \- Spark context used to save model data.  
     `path` \- Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.