 Learning analytical models from noisy data remains challenging and depends essentially on the noise level. The authors analyze the transition of the model learning problem from a low-noise phase to a phase where noise is too high for the underlying model to be learned by any method, an estimate upper-bounds for the transition noise. This article was authored by Oscar Fajardo Fontiveros, Ignacy Ricott, Harry Adelos Rios and others.