Struct ElasticNetFit 

pub struct ElasticNetFit {

    pub lambda: f64,
    pub alpha: f64,
    pub intercept: f64,
    pub coefficients: Vec<f64>,
    pub fitted_values: Vec<f64>,
    pub residuals: Vec<f64>,
    pub n_nonzero: usize,
    pub iterations: usize,
    pub converged: bool,
    pub r_squared: f64,
    pub adj_r_squared: f64,
    pub mse: f64,
    pub rmse: f64,
    pub mae: f64,
    pub log_likelihood: f64,
    pub aic: f64,
    pub bic: f64,
}

Result of an elastic net fit.

Contains the fitted model coefficients, convergence information, and diagnostic metrics.

Fields

• lambda - The regularization strength used
• alpha - The elastic net mixing parameter (0 = Ridge, 1 = Lasso)
• intercept - Intercept coefficient (never penalized)
• coefficients - Slope coefficients (may be sparse for high alpha)
• fitted_values - Predicted values on training data
• residuals - Residuals (y - fitted_values)
• n_nonzero - Number of non-zero coefficients (excluding intercept)
• iterations - Number of coordinate descent iterations performed
• converged - Whether the algorithm converged
• r_squared - Coefficient of determination
• adj_r_squared - Adjusted R²
• mse - Mean squared error
• rmse - Root mean squared error
• mae - Mean absolute error
• log_likelihood - Log-likelihood of the model (for model comparison)
• aic - Akaike Information Criterion (lower = better)
• bic - Bayesian Information Criterion (lower = better)

Example

let fit = elastic_net_fit(&x, &y, &options).unwrap();

// Access fit results
println!("Lambda: {}, Alpha: {}", fit.lambda, fit.alpha);
println!("Non-zero coefficients: {}", fit.n_nonzero);
println!("Converged: {}", fit.converged);
println!("R²: {}", fit.r_squared);
println!("AIC: {}", fit.aic);

Fields

lambda: f64

alpha: f64

intercept: f64

coefficients: Vec<f64>

fitted_values: Vec<f64>

residuals: Vec<f64>

n_nonzero: usize

iterations: usize

converged: bool

r_squared: f64

adj_r_squared: f64

mse: f64

rmse: f64

mae: f64

log_likelihood: f64

aic: f64

bic: f64

Trait Implementations

impl Clone for ElasticNetFit

fn clone(&self) -> ElasticNetFit

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ElasticNetFit

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for ElasticNetFit

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl ModelLoad for ElasticNetFit

fn load(path: &str) -> Result<Self>

Load a model from a file.

fn from_serialized(model: SerializedModel) -> Result<Self>

Load a model from an already-deserialized wrapper.

fn model_type() -> ModelType

Get the model type identifier.

impl ModelSave for ElasticNetFit

fn save_with_name(&self, path: &str, name: Option<String>) -> Result<()>

Save the model to a file with a custom name.

fn model_type() -> ModelType

Get the model type identifier.

fn save(&self, path: &str) -> Result<(), Error>

Save the model to a file.

impl Serialize for ElasticNetFit

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.