PMLB: a large benchmark suite for machine learning evaluation and comparison
Abstract
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine...
Paper Details
Title
PMLB: a large benchmark suite for machine learning evaluation and comparison
Published Date
Dec 1, 2017
Journal
Volume
10
Issue
1
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