SkData: data sets and algorithm evaluation protocols in Python. (28th July 2015)

• Record Type: Journal Article
• Authors: Bergstra, James; Pinto, Nicolas; Cox, David D
• Is Part Of: Computational science & discovery. Volume 8:Number 1(2015)
• Publication Date: 2015-07-28
• Subjects: Python -- machine learning -- data management -- reproducible science; Science -- Computer simulation -- Periodicals; Technology -- Computer simulation -- Periodicals; Science -- Data processing -- Periodicals; Technology -- Data processing -- Periodicals; Research -- Methodology -- Periodicals; Research -- Periodicals; Periodicals; 501.13
• DOI: 10.1088/1749-4699/8/1/014007
• Languages: English
• ISSNs: 1749-4699

Abstract

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.