# Naive Bayes classifiers

A family of simple probabilistic, multiclass classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between every pair of features.

Naive Bayes can be trained very efficiently. With a single pass over the training data, it computes the conditional probability distribution of each feature given each label. For prediction, it applies Bayes’ theorem to compute the conditional probability distribution of each label given an observation.

```
import org.apache.spark.ml.classification.NaiveBayes
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// Load the data stored in LIBSVM format as a DataFrame.
val data = spark.read.format("libsvm").load("file:///opt/spark/data/mllib/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3), seed = 1234L)
// Train a NaiveBayes model.
val model = new NaiveBayes()
.fit(trainingData)
// Select example rows to display.
val predictions = model.transform(testData)
predictions.show()
// Select (prediction, true label) and compute test error
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy = evaluator.evaluate(predictions) 
println(s"Test set accuracy = $accuracy")

/*
Output:

+-----+--------------------+--------------------+-----------+----------+
|label|            features|       rawPrediction|probability|prediction|
+-----+--------------------+--------------------+-----------+----------+
|  0.0|(692,[95,96,97,12...|[-173678.60946628...|  [1.0,0.0]|       0.0|
|  0.0|(692,[98,99,100,1...|[-178107.24302988...|  [1.0,0.0]|       0.0|
|  0.0|(692,[100,101,102...|[-100020.80519087...|  [1.0,0.0]|       0.0|
|  0.0|(692,[124,125,126...|[-183521.85526462...|  [1.0,0.0]|       0.0|
|  0.0|(692,[127,128,129...|[-183004.12461660...|  [1.0,0.0]|       0.0|
|  0.0|(692,[128,129,130...|[-246722.96394714...|  [1.0,0.0]|       0.0|
|  0.0|(692,[152,153,154...|[-208696.01108598...|  [1.0,0.0]|       0.0|
|  0.0|(692,[153,154,155...|[-261509.59951302...|  [1.0,0.0]|       0.0|
|  0.0|(692,[154,155,156...|[-217654.71748256...|  [1.0,0.0]|       0.0|
|  0.0|(692,[181,182,183...|[-155287.07585335...|  [1.0,0.0]|       0.0|
|  1.0|(692,[99,100,101,...|[-145981.83877498...|  [0.0,1.0]|       1.0|
|  1.0|(692,[100,101,102...|[-147685.13694275...|  [0.0,1.0]|       1.0|
|  1.0|(692,[123,124,125...|[-139521.98499849...|  [0.0,1.0]|       1.0|
|  1.0|(692,[124,125,126...|[-129375.46702012...|  [0.0,1.0]|       1.0|
|  1.0|(692,[126,127,128...|[-145809.08230799...|  [0.0,1.0]|       1.0|
|  1.0|(692,[127,128,129...|[-132670.15737290...|  [0.0,1.0]|       1.0|
|  1.0|(692,[128,129,130...|[-100206.72054749...|  [0.0,1.0]|       1.0|
|  1.0|(692,[129,130,131...|[-129639.09694930...|  [0.0,1.0]|       1.0|
|  1.0|(692,[129,130,131...|[-143628.65574273...|  [0.0,1.0]|       1.0|
|  1.0|(692,[129,130,131...|[-129238.74023248...|  [0.0,1.0]|       1.0|
+-----+--------------------+--------------------+-----------+----------+
only showing top 20 rows

Test set accuracy = 1.0


*/
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://george-jen.gitbook.io/data-science-and-apache-spark/naive-bayes-classifiers.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.