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Diversified Ensembling: An Experiment in Crowdsourced Machine Learning
Feb. 19, 2024, 5:42 a.m. | Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
cs.LG updates on arXiv.org arxiv.org
Abstract: Crowdsourced machine learning on competition platforms such as Kaggle is a popular and often effective method for generating accurate models. Typically, teams vie for the most accurate model, as measured by overall error on a holdout set, and it is common towards the end of such competitions for teams at the top of the leaderboard to ensemble or average their models outside the platform mechanism to get the final, best global model. In arXiv:2201.10408, the …
abstract arxiv competition competitions cs.cy cs.lg error experiment kaggle machine machine learning platforms popular set teams type
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