What is "naive" in a naive Bayes classifier?

algorithm classification naivebayes
Peddler picture Peddler · May 16, 2012 · Viewed 21.2k times · Source

What is naive about Naive Bayes?

Answer

laurent picture laurent · May 16, 2012

There's actually a very good example on Wikipedia:

In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter. Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.

Basically, it's "naive" because it makes assumptions that may or may not turn out to be correct.

Related questions

A simple explanation of Naive Bayes Classification

I am finding it hard to understand the process of Naive Bayes, and I was wondering if someone could explain it with a simple step by step process in English. I understand it takes comparisons by times occurred as a …

Read More
How to calculate classification error rate

Alright. Now this question is pretty hard. I am going to give you an example. Now the left numbers are my algorithm classification and the right numbers are the original class numbers 177 86 177 86 177 86 177 86 177 86 177 86 177 86 177 86 177 86 177 89 177 89 177 89 177 89 177 89 177 89 177 89 So here my algorithm merged 2 different classes into 1. …

Read More
Best learning algorithm to make a decision tree in java?

I have a datasets with information like age, city, age of children, ... and a result (confirm, accept). To help modelisation of "workflow", I want to create automatically a decision tree based on previous datasets. I have take a look at …

Read More