Automating data science

Author:

De Bie Tijl1,De Raedt Luc2,Hernández-Orallo José3,Hoos Holger H.4,Smyth Padhraic5,Williams Christopher K. I.6

Affiliation:

1. Ghent University, Belgium

2. KU Leuven, Belgium and Örebro University, Sweden

3. Universitat Politècnica de València, Spain

4. Leiden University, The Netherlands and University of British Columbia in Vancouver, Canada

5. University of California, Irvine

6. University of Edinburgh, U.K and Alan Turing Institute, London, U.K

Abstract

Given the complexity of data science projects and related demand for human expertise, automation has the potential to transform the data science process.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference51 articles.

1. Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conf. on Human Factors;Amershi S.;Computing Systems,2019

2. Bjorkman , A. et al. Plant functional trait change across a warming tundra biome. Nature 562, 7725 ( 2018 ), 57. Bjorkman, A. et al. Plant functional trait change across a warming tundra biome. Nature 562, 7725 (2018), 57.

3. An inductive database system based on virtual mining views

4. Brazdil , P. , Carrier , C. , Soares , C. , and Vilalta , R . Metalearning: Applications to Data Mining . Springer Science & Business Media , 2008 . Brazdil, P., Carrier, C., Soares, C., and Vilalta, R. Metalearning: Applications to Data Mining. Springer Science & Business Media, 2008.

5. Chapman , P. et al. CRISP-DM 1.0 Step-by-step data mining guide , 2000 . Chapman, P. et al. CRISP-DM 1.0 Step-by-step data mining guide, 2000.

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