Classifiers, table of contents

Chapter 13.5: Naive bayes classifier

This section describes naive Bayes density estimation and classification.

- 13.5.1. Introduction

# 13.5.1. Introduction ↩

Naive Bayes classifier is implemented by the `sdnbayes`

function. For each
feature, it estimates a class-conditional distribution using a
histogram. Assuming independence of features, the per-class output is
computed as a product of per-feature class conditional densities.

By default, `sdnbayes`

uses histograms with 20 bins:

**>> a**
3000 by 2 sddata, 3 classes: 'apple'(1000) 'banana'(1000) 'stone'(1000)
**>> p=**`sdnbayes`

(a)
sequential pipeline 2x1 'naive Bayes+Decision'
1 naive Bayes 2x3
2 Decision 3x1 weighting, 3 classes
**>> **`sdscatter`

(a,p)

The number of histogram bins may be fixed manually with the second parameter (or 'bins' option):

**>> p=**`sdnbayes`

(a,50)
sequential pipeline 2x1 'naive Bayes+Decision'
1 naive Bayes 2x3
2 Decision 3x1 weighting, 3 classes