What non-negative linear models are supported/planned in scikit-learn?

Scikit-learn is a popular machine learning library in Python that provides tools for various tasks, including regression and classification. One important feature of scikit-learn is its support for non-negative linear models. These models are useful in situations where the predicted values should be constrained to be non-negative, such as in the case of count data or when dealing with non-negative quantities.

Currently, scikit-learn supports two non-negative linear models: Non-negative Least Squares (NNLS) and Non-negative Elastic Net (NEN).

Non-negative Least Squares (NNLS)

Non-negative Least Squares is a method for solving linear regression problems where the coefficients are constrained to be non-negative. This can be useful when the data has a non-negative structure and negative coefficients do not make sense.

Scikit-learn provides an implementation of NNLS through the sklearn.linear_model.nnls module. This module allows you to fit a non-negative linear model to your data and make predictions using the fitted model.

Non-negative Elastic Net (NEN)

Non-negative Elastic Net is a regularized regression method that combines the penalties of L1 (Lasso) and L2 (Ridge) regularization with the constraint that the coefficients are non-negative. This can help improve the sparsity and stability of the model, especially in high-dimensional settings.

While scikit-learn currently does not have a built-in implementation of Non-negative Elastic Net, there are plans to add this functionality in future releases. In the meantime, you can use other libraries such as nnetsauce or glmnet_python to fit non-negative elastic net models.

Overall, non-negative linear models are a powerful tool in machine learning, and having support for these models in scikit-learn allows users to easily incorporate non-negativity constraints into their predictive models.