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

Scikit-learn is a popular machine learning library in Python that provides various algorithms for classification, regression, clustering, and dimensionality reduction. One important group of models supported in scikit-learn are non-negative linear models. These models are used when the target variable should be non-negative, such as in cases where the target represents counts or probabilities.

Some of the non-negative linear models supported in scikit-learn include:

• Non-negative Least Squares (NNLS): This algorithm minimizes the sum of squared differences between the observed values and the predicted values, subject to the constraint that all coefficients are non-negative.
• Non-negative Elastic Net: This model combines the non-negative constraint with the elastic net regularization, which is a linear combination of L1 and L2 penalties. This allows for feature selection and handling multicollinearity in the data.
• Non-negative Poisson regression: This model is used for count data and enforces non-negativity on the coefficients. It fits a Poisson distribution to the data and estimates the parameters using a maximum likelihood approach.

There are also plans to support other non-negative linear models in future versions of scikit-learn. Some of the models that may be included in the future are:

• Non-negative Matrix Factorization (NMF): This model is used for dimensionality reduction and feature extraction. It factorizes the input data matrix into two non-negative matrices, which can be interpreted as latent features.
• Non-negative Tucker Decomposition: This model is an extension of the NMF, which decomposes a tensor into a core tensor and factor matrices with non-negative elements. It can be used for higher-dimensional data analysis.

Overall, non-negative linear models are important in cases where the target variable should be non-negative, and scikit-learn provides a variety of algorithms to handle such scenarios. With the continuous development of the library, we can expect more non-negative linear models to be added in the future, enhancing the capabilities of scikit-learn for various machine learning tasks.