Classo

Classo is a term used in statistics and machine learning to describe a family of methods that apply L1 regularization to classification and regression tasks in order to produce sparse models. The concept appears in research literature and software documentation, where it is often discussed as a classification-focused variant of the lasso.

Mathematically, classo problems typically minimize a convex loss function L(y, Xβ) plus a penalty term λ||β||1, where β are the model coefficients. The L1 penalty encourages sparsity, enabling simultaneous prediction and variable selection. In practice, the loss can be paired with additional structure or constraints, giving rise to variants such as constrained lasso, group lasso, or multi-task lasso, depending on the data and problem context.

In classification contexts, classo is used to identify a small set of predictive features while estimating decision boundaries. It is particularly advantageous in high-dimensional settings where the number of features is large relative to the sample size, such as in genomics, text classification, and image analysis. The method supports or integrates with common classification losses, including logistic and hinge losses, and can be adapted to generalized linear models.

Implementation typically involves standardizing predictors, selecting the regularization parameter λ via cross-validation, and solving the resulting convex optimization problem with algorithms such as coordinate descent, proximal gradient methods, or ADMM. Various software environments offer lasso-type functionality, and some packages explicitly label their approach as classo or as a specialized lasso variant for classification.

See also Lasso, Elastic net, Sparse modeling, Regularization.