Struct GaussianNB

pub struct GaussianNB { /* private fields */ }

A Gaussian Naive Bayes classifier.

Parameters

• var_smoothing: Portion of the largest variance of all features to add to variances for stability.
• use_unbiased_variance: If true, uses Bessel’s correction (dividing by (n-1)); otherwise divides by n.

Implementations

impl GaussianNB

pub fn new(var_smoothing: f64, use_unbiased_variance: bool) -> Self

Create a new GaussianNB.

Arguments

• var_smoothing - small float added to variances for numerical stability.
• use_unbiased_variance - whether to apply Bessel’s correction (divide by n-1).

pub fn fit(&mut self, x: &Matrix<f64>, y: &Matrix<f64>)

Fit the model according to the training data x and labels y.

Panics

Panics if x or y is empty, or if their dimensions disagree.

pub fn predict(&self, x: &Matrix<f64>) -> Matrix<f64>

Perform classification on an array of test vectors x.