Struct linfa_linear::LinearRegression

pub struct LinearRegression { /* private fields */ }

An ordinary least squares linear regression model.

LinearRegression fits a linear model to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.

Ordinary least squares regression solves the overconstrainted model

y = Ax + b

by finding x and b which minimize the L_2 norm ||y - Ax - b||_2.

It currently uses the Moore-Penrose pseudo-inverse to solve y - b = Ax.

/// ## 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 copy 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

This method tests for self and other values to be equal, and is used by ==.

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

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

impl Eq for LinearRegression

impl StructuralEq for LinearRegression

impl StructuralPartialEq for LinearRegression