pub type LogisticRegression<F> = LogisticRegressionParams<F, Ix1>;Expand description
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);Aliased Type§
struct LogisticRegression<F>(/* private fields */);