[1904.12320] Real numbers, data science and chaos: How to fit any dataset with a single parameter

We show how any dataset of any modality (time-series, images, sound...) can be approximated by a well-behaved (continuous, differentiable...) scalar function with a single real-valued parameter. Building upon elementary concepts from chaos theory, we adopt a pedagogical approach demonstrating how to adjust this parameter in order to achieve arbitrary precision fit to all samples of the data. Targeting an audience of data scientists with a taste for the curious and unusual, the results presented here expand on previous similar observations regarding expressiveness power and generalization of machine learning models.

Real numbers, data science and chaos: How to fit any dataset with a single parameter Laurent Boué SAP Labs arXiv:1904.12320v1 [cs.LG] 28 Apr 2019 Abstract We show how any dataset of any modality…

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