Pyglmnet: Python implementation of elastic-net regularized generalized linear models
Journal of Open Source Software, 2020•biblio.ugent.be
Generalized linear models (GLMs) are well-established tools for regression and
classification and are widely applied across the sciences, economics, business, and finance.
Owing to their convex loss, they are easy and efficient to fit. Moreover, they are relatively
easy to interpret because of their well-defined noise distributions and point-wise
nonlinearities.
classification and are widely applied across the sciences, economics, business, and finance.
Owing to their convex loss, they are easy and efficient to fit. Moreover, they are relatively
easy to interpret because of their well-defined noise distributions and point-wise
nonlinearities.
Summary
Generalized linear models (GLMs) are well-established tools for regression and classification and are widely applied across the sciences, economics, business, and finance. Owing to their convex loss, they are easy and efficient to fit. Moreover, they are relatively easy to interpret because of their well-defined noise distributions and point-wise nonlinearities.
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