2.1 KiB
Multibayes
Multiclass naive Bayesian document classification.
Often in document classification, a document may have more than one relevant classification -- a question on stackoverflow 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:
classifier.Add("A new document", []string{"class1", "class2"})
Posterior probabilities for a new document are calculated by calling the Posterior method:
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
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]