Type Definition linfa_logistic::LogisticRegression

pub type LogisticRegression<F> = LogisticRegressionParams<F, Ix1>;

A two-class logistic regression model.

Logistic regression combines linear models with the sigmoid function sigm(x) = 1/(1+exp(-x)) to learn a family of functions that map the feature space to [0,1].

Logistic regression is used in binary classification by interpreting the predicted value as the probability that the sample has label 1. A threshold can be set in the fitted model to decide the minimum probability needed to classify a sample as 1, which defaults to 0.5.

In this implementation any binary set of labels can be used, not necessarily 0 and 1.

l2 regularization is used by this algorithm and is weighted by parameter alpha. Setting alpha close to zero removes regularization and the problem solved minimizes only the empirical risk. On the other hand, setting alpha to a high value increases the weight of the l2 norm of the linear model coefficients in the cost function.

Examples

Here’s an example on how to train a logistic regression model on the winequality dataset

use linfa::traits::{Fit, Predict};
use linfa_logistic::LogisticRegression;

// Example on using binary labels different from 0 and 1
let dataset = linfa_datasets::winequality().map_targets(|x| if *x > 6 { "good" } else { "bad" });
let model = LogisticRegression::default().fit(&dataset).unwrap();
let prediction = model.predict(&dataset);