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What does "Naive" Bayes mean in Machine learning?

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  • Are the first couple of of paragraphs of https://en.wikipedia.org/wiki/Naive_Bayes_classifier any use? If not, could you clarify the question? – DNA Feb 26 '16 at 17:38

3 Answers3

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Naive Bayes in machine learning typically refers to a set of supervised learning algorithms that apply the Bayes' theorem. It's essentially a "classifier" that helps you classify things based on a series of independent "naive" assumptions. For example if you wanted to use machine learning to help you identify potential fruit... taking a banana, its curved, yellow, and may be 10 inches long. Each of those properties, 'curved', 'yellow', '10 inches long' are all independent properties that come together to form a 'probability' that a fruit is a banana. With this 'naive' bayes classifier, in the future if there are other kinds of 'fruit' or different images, descriptions of fruit that have similar properties, using machine learning, your naive bayes classifier can classify those future fruits or unknown things as bananas correctly... (or incorrectly) in which you'll probably want to identify more 'naive' features to make your classifier more accurate like for example, there might be a 'blackened tip' or 'have a slight greenish color'.

Rolando
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It is called naïve because the model assumes independence between the features. This is a strong assumption which usually is not correct and that is the reason of the name.

Nevertheless, naïve Bayes is quite efficient and in practice is known for giving good results.

Juan Leni
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A naive Bayes classifier is an algorithm that uses Bayes' theorem to classify objects. Naive Bayes classifiers assume strong, or naive, independence between attributes of data points. Popular uses of naive Bayes classifiers include spam filters, text analysis and medical diagnosis.

What makes a naive Bayes classifier naive is its assumption that all attributes of a data point under consideration are independent of each other. A classifier sorting fruits into apples and oranges would know that apples are red, round and are a certain size, but would not assume all these things at once. Oranges are round too, after all.

A naive Bayes classifier is not a single algorithm, but a family of machine learning algorithms that make uses of statistical independence. These algorithms are relatively easy to write and run more efficiently than more complex Bayes algorithms.

The most popular application is spam filters. A spam filter looks at email messages for certain key words and puts them in a spam folder if they match.

Despite the name, the more data it gets, the more accurate a naive Bayes classifier becomes, such as from a user flagging email messages in an inbox for spam.

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