Struct linfa_linear::LinearRegression

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pub struct LinearRegression { /* private fields */ }

Expand description

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

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impl LinearRegression

Configure and fit a linear regression model

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pub fn new() -> LinearRegression

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

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

Configure the linear regression model to fit an intercept.

Trait Implementations

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impl Clone for LinearRegression

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fn clone(&self) -> LinearRegression

Returns a copy of the value. Read more

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

Performs copy-assignment from source. Read more

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impl Debug for LinearRegression

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

Formats the value using the given formatter. Read more

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impl Default for LinearRegression

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fn default() -> Self

Returns the “default value” for a type. Read more

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impl<'de> Deserialize<'de> for LinearRegression

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fn deserialize<D>(deserializer: D) -> Result<Self, D::Error> where
__D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer. Read more

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

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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.