https://github.com/lytics/multibayes

Multiclass Naive Bayesian Classification
https://github.com/lytics/multibayes

Last synced: about 2 months ago
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Multiclass Naive Bayesian Classification

• Host: GitHub
• URL: https://github.com/lytics/multibayes
• Owner: lytics
• Created: 2014-11-07T18:11:01.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2018-08-10T09:24:18.000Z (over 6 years ago)
• Last Synced: 2024-08-01T04:02:18.521Z (4 months ago)
• Language: Go
• Homepage:
• Size: 55.7 KB
• Stars: 76
• Watchers: 29
• Forks: 9
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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• awesome-golang-ai - multibayes

README

        
Multibayes

==========

[![Build Status](https://travis-ci.org/lytics/multibayes.svg?branch=master)](https://travis-ci.org/lytics/multibayes) [![GoDoc](https://godoc.org/github.com/lytics/multibayes?status.svg)](https://godoc.org/github.com/lytics/multibayes)

Multiclass naive Bayesian document classification.

Often in document classification, a document may have more than one relevant classification -- a question on [stackoverflow](http://stackoverflow.com) might have tags "go", "map", and "interface".

While multinomial Bayesian classification offers a one-of-many classification, multibayes offers tools for many-of-many classification.  The multibayes library strives to offer efficient storage and calculation of multiple Bayesian posterior classification probabilities.

## Usage

A new classifier is created with the `NewClassifier` function, and can be trained by adding documents and classes by calling the `Add` method:

```go

classifier.Add("A new document", []string{"class1", "class2"})

```

Posterior probabilities for a new document are calculated by calling the `Posterior` method:

```go

classifier.Posterior("Another new document")

```

A posterior class probability is returned for each class observed in the training set, which the user can use to determine class assignment.  A user can then assign classifications according to his or her own heuristics -- for example, by using all classes that yield a posterior probability greater than 0.8

## Example

```go

documents := []struct {

	Text    string

	Classes []string

}{

	{

		Text:    "My dog has fleas.",

		Classes: []string{"vet"},

	},

	{

		Text:    "My cat has ebola.",

		Classes: []string{"vet", "cdc"},

	},

	{

		Text:    "Aaron has ebola.",

		Classes: []string{"cdc"},

	},

}

classifier := NewClassifier()

classifier.MinClassSize = 0

// train the classifier

for _, document := range documents {

	classifier.Add(document.Text, document.Classes)

}

// predict new classes

probs := classifier.Posterior("Aaron's dog has fleas.")

fmt.Printf("Posterior Probabilities: %+v\n", probs)

// Posterior Probabilities: map[vet:0.8571 cdc:0.2727]

```