Laurent Bou´e, Real numbers, data science and chaos: How to fit any dataset with a single parameter, arXiv:1904.12320v1 [cs.LG] 28 Apr 2019.
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 [1] regarding expressiveness power and generalization of machine learning models.