# Naive Bayes¶

A fast and simple probabilistic classifier based on Bayes’ theorem with the assumption of feature independence.

## Signals¶

**Inputs**:

**Data**A data set

**Preprocessor**Preprocessing method(s)

**Outputs**:

**Learner**A naive bayes learning algorithm with settings as specified in the dialog.

**Model**A trained classifier. Output signal sent only if input

*Data*is present.

## Description¶

**Naive Bayes** learns a Naive Bayesian model from the data.

It only works for classification tasks.

This widget has two options: the name under which it will appear in
other widgets and producing a report. The default name is *Naive Bayes*. When you change it,
you need to press *Apply*.

## Examples¶

Here, we present two uses of this widget. First, we compare the results of the
**Naive Bayes** with another model, the Random Forest. We connect *iris* data from File to Test&Score. We also connect **Naive Bayes** and Random Forest to **Test & Score** and observe their prediction scores.

The second schema shows the quality of predictions made with **Naive Bayes**. We feed the Test&Score widget a Naive Bayes learner and then send the data to the Confusion Matrix. We also connect Scatterplot with **File**. Then we select the misclassified instances in the **Confusion Matrix** and show feed them to Scatterplot. The bold dots in the scatterplot are the misclassified instances from **Naive Bayes**.