How to Host An Effective Data Competition: Statistical Advice for Competition Design and Analysis
Document Type
Article
Publication Date
2019
Keywords
competition leaderboard, detection, exploratory data analysis, generalized linear models, identification, Kaggle competition, location, logistic regression
Digital Object Identifier (DOI)
https://doi.org/10.1002/sam.11404
Abstract
Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a postcompetition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The postcompetition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual subquestions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Statistical Analysis and Data Mining: The ASA Data Science Journal, v. 12, issue 4, p. 271-289
Scholar Commons Citation
Anderson-Cook, Christine M.; Myers, Kary L.; Lu, Lu; Fugate, Michael L.; Quinlan, Kevin R.; and Pawley, Norma, "How to Host An Effective Data Competition: Statistical Advice for Competition Design and Analysis" (2019). Mathematics and Statistics Faculty Publications. 143.
https://digitalcommons.usf.edu/mth_facpub/143
