SkData: data sets and algorithm evaluation protocols in Python. (28th July 2015)
- Record Type:
- Journal Article
- Title:
- SkData: data sets and algorithm evaluation protocols in Python. (28th July 2015)
- Main Title:
- SkData: data sets and algorithm evaluation protocols in Python
- Authors:
- Bergstra, James
Pinto, Nicolas
Cox, David D - 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.
- Is Part Of:
- Computational science & discovery. Volume 8:Number 1(2015)
- Journal:
- Computational science & discovery
- Issue:
- Volume 8:Number 1(2015)
- Issue Display:
- Volume 8, Issue 1 (2015)
- Year:
- 2015
- Volume:
- 8
- Issue:
- 1
- Issue Sort Value:
- 2015-0008-0001-0000
- Page Start:
- Page End:
- 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 - Journal URLs:
- http://iopscience.iop.org/1749-4699 ↗
http://www.iop.org/EJ/journal/CSD ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1749-4699/8/1/014007 ↗
- Languages:
- English
- ISSNs:
- 1749-4699
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 6519.xml