Module rusty_machine::learning::naive_bayes
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[src]
Naive Bayes Classifiers
The classifier supports Gaussian, Bernoulli and Multinomial distributions.
A naive Bayes classifier works by treating the features of each input as independent observations. Under this assumption we utilize Bayes' rule to compute the probability that each input belongs to a given class.
Examples
use rusty_machine::learning::naive_bayes::{NaiveBayes, Gaussian}; use rusty_machine::linalg::Matrix; use rusty_machine::learning::SupModel; let inputs = Matrix::new(6, 2, vec![1.0, 1.1, 1.1, 0.9, 2.2, 2.3, 2.5, 2.7, 5.2, 4.3, 6.2, 7.3]); let targets = Matrix::new(6,3, vec![1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]); // Create a Gaussian Naive Bayes classifier. let mut model = NaiveBayes::<Gaussian>::new(); // Train the model. model.train(&inputs, &targets).unwrap(); // Predict the classes on the input data let outputs = model.predict(&inputs).unwrap(); // Will output the target classes - otherwise our classifier is bad! println!("Final outputs --\n{}", outputs);
Structs
Bernoulli |
The Bernoulli Naive Bayes model distribution. |
Gaussian |
The Gaussian Naive Bayes model distribution. |
Multinomial |
The Multinomial Naive Bayes model distribution. |
NaiveBayes |
The Naive Bayes model. |
Traits
Distribution |
Naive Bayes Distribution. |