64 lines
2.1 KiB
Markdown
64 lines
2.1 KiB
Markdown
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Multibayes
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==========
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[](https://travis-ci.org/lytics/multibayes) [](https://godoc.org/github.com/lytics/multibayes)
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Multiclass naive Bayesian document classification.
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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".
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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.
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## Usage
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A new classifier is created with the `NewClassifier` function, and can be trained by adding documents and classes by calling the `Add` method:
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```go
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classifier.Add("A new document", []string{"class1", "class2"})
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```
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Posterior probabilities for a new document are calculated by calling the `Posterior` method:
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```go
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classifier.Posterior("Another new document")
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```
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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
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## Example
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```go
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documents := []struct {
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Text string
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Classes []string
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}{
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{
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Text: "My dog has fleas.",
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Classes: []string{"vet"},
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},
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{
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Text: "My cat has ebola.",
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Classes: []string{"vet", "cdc"},
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},
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{
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Text: "Aaron has ebola.",
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Classes: []string{"cdc"},
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},
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}
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classifier := NewClassifier()
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classifier.MinClassSize = 0
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// train the classifier
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for _, document := range documents {
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classifier.Add(document.Text, document.Classes)
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}
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// predict new classes
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probs := classifier.Posterior("Aaron's dog has fleas.")
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fmt.Printf("Posterior Probabilities: %+v\n", probs)
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// Posterior Probabilities: map[vet:0.8571 cdc:0.2727]
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```
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