Mojo struct
GaussianNB
@memory_only
struct GaussianNB
Gaussian Naive Bayes (GaussianNB).
Assumes the likelihood of each feature, conditioned on the class, follows a Gaussian distribution. Suited for continuous, real-valued features.
Aliases
MODEL_ID = 7
Fields
- var_smoothing (
Float32): Portion of the largest variance of all features that is added to variances for calculation stability.
Implemented traits
AnyType, CV, Copyable, Deinitable, Movable
Methods
__init__
fn def __init__(out self, var_smoothing: Float32 = 1.0E-8)
Args:
- var_smoothing (
Float32) - self (
Self)
Returns:
Self
fn def __init__(out self, params: Dict[String, String])
Construct from a hyperparameter dictionary.
Args:
- params (
Dict[String, String]) - self (
Self)
Returns:
Self
Raises:
fit
fn def fit(mut self, X: Matrix, y: Matrix)
Fit Gaussian Naive Bayes.
Args:
- self (
Self) - X (
Matrix): Training features of shape (n_samples, n_features). - y (
Matrix): Training labels of shape (n_samples, 1), encoded as contiguous non-negative integers starting at 0.
Raises:
predict
fn def predict(self, X: Matrix) -> Matrix
Predict class for X.
Args:
- self (
Self) - X (
Matrix)
Returns:
Matrix: The predicted classes.
Raises:
save
fn def save(self, path: String)
Save model data necessary for prediction to the specified path.
Args:
- self (
Self) - path (
String)
Raises:
load
@staticmethod
fn def load(path: String) -> Self
Load a saved model from the specified path for prediction.
Args:
- path (
String)
Returns:
Self
Raises: