Struct LinearRegression

pub struct LinearRegression { /* private fields */ }

An ordinary least squares univariate linear regression model.

Given predictors x and responses y ordinary least squares linear regression estimates a model of the form

y = xW + b

by finding a matrix W and a vector b which minimize the sum of the squared L_2 norms ||y_j - x_jW - b||_2^2 for a dataset {(x_j, y_j) for j in 1..=n_samples}.

The algorithm is only implemented for univariate regression. This means that b and y are scalars and W is just one column.

Examples

Here’s an example on how to train a linear regression model on the diabetes dataset

use linfa::traits::{Fit, Predict};
use linfa_linear::LinearRegression;
use linfa::prelude::SingleTargetRegression;

let dataset = linfa_datasets::diabetes();
let model = LinearRegression::default().fit(&dataset).unwrap();
let pred = model.predict(&dataset);
let r2 = pred.r2(&dataset).unwrap();
println!("r2 from prediction: {}", r2);

Implementations

impl LinearRegression

Configure and fit a linear regression model

pub fn new() -> LinearRegression

Create a default linear regression model. By default, an intercept will be fitted.

pub fn with_intercept(self, intercept: bool) -> Self

Configure the linear regression model to fit an intercept.

Trait Implementations

impl Clone for LinearRegression

fn clone(&self) -> LinearRegression

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LinearRegression

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

Formats the value using the given formatter.

impl Default for LinearRegression

fn default() -> Self

Returns the “default value” for a type.

impl<F: Float, D: Data<Elem = F>, T: AsSingleTargets<Elem = F>> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, LinearError<F>> for LinearRegression

fn fit( &self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T>, ) -> Result<Self::Object, F>

Fit a linear regression model given a feature matrix X and a target variable y.

The feature matrix X must have shape (n_samples, n_features)

The target variable y must have shape (n_samples)

Returns a FittedLinearRegression object which contains the fitted parameters and can be used to predict values of the target variable for new feature values.

type Object = FittedLinearRegression<F>

impl PartialEq for LinearRegression

fn eq(&self, other: &LinearRegression) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Eq for LinearRegression

impl StructuralPartialEq for LinearRegression