Naive Bayes #

Naive Bayes is a multiclass classifier. Based on Bayes’ theorem, it assumes that there is strong (naive) independence between every pair of features.

Input Columns #

Param name	Type	Default	Description
featuresCol	Vector	`"features"`	Feature vector
labelCol	Integer	`"label"`	Label to predict

Output Columns #

Param name	Type	Default	Description
predictionCol	Integer	`"prediction"`	Predicted label

Parameters #

Below are parameters required by NaiveBayesModel.

Key	Default	Type	Required	Description
modelType	`"multinomial"`	String	no	The model type. Supported values: “multinomial”
featuresCol	`"features"`	String	no	Features column name.
predictionCol	`"prediction"`	String	no	Prediction column name.

NaiveBayes needs parameters above and also below.

Key	Default	Type	Required	Description
labelCol	`"label"`	String	no	Label column name.
smoothing	`1.0`	Double	no	The smoothing parameter.

Examples #

import org.apache.flink.ml.classification.naivebayes.NaiveBayes;
import org.apache.flink.ml.classification.naivebayes.NaiveBayesModel;
import org.apache.flink.ml.linalg.Vectors;

List<Row> trainData =
  Arrays.asList(
  Row.of(Vectors.dense(0, 0.), 11),
  Row.of(Vectors.dense(1, 0), 10),
  Row.of(Vectors.dense(1, 1.), 10));

Table trainTable = tEnv.fromDataStream(env.fromCollection(trainData)).as("features", "label");

List<Row> predictData =
  Arrays.asList(
  Row.of(Vectors.dense(0, 1.)),
  Row.of(Vectors.dense(0, 0.)),
  Row.of(Vectors.dense(1, 0)),
  Row.of(Vectors.dense(1, 1.)));

Table predictTable = tEnv.fromDataStream(env.fromCollection(predictData)).as("features");

NaiveBayes estimator =
  new NaiveBayes()
  .setSmoothing(1.0)
  .setFeaturesCol("features")
  .setLabelCol("label")
  .setPredictionCol("prediction")
  .setModelType("multinomial");

NaiveBayesModel model = estimator.fit(trainTable);
Table outputTable = model.transform(predictTable)[0];

outputTable.execute().print();