Siglearn is an experimental repository for providing a well-defined, scikit-learn-style API for performing modeling and machine learning tasks on noisy data.
Often in scientific detector data, we have some estimate of the error in observed points. Unfortunately, most classic machine learning approaches are not built with data errors in mind. Siglearn is an attempt to begin collecting implementations of algorithms which do handle data errors.
The code is very much under development; if you're interested in helping, I would love your contribution!