SkData: data sets and algorithm evaluation protocols in Python. (28th July 2015) - Bergstra, James; Pinto, Nicolas; Cox, David D
Abstract: Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as ( x, y ) pairs with vector-valued input x and integer class label y . Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.