Abstract: We apply machine learning to understand fundamental aspects of holographic duality, specifically the entropies obtained from the apparent and event horizon areas [1]. We show that simple features of only the time series of the pressure anisotropy can predict the areas of the apparent and event horizons in the dual bulk geometry at all times. Given that simple Vaidya-type metrics constructed just from the apparent and event horizon areas can be used to approximately obtain unequal time correlation functions [2], we argue that the corresponding entropy functions are the measures of information that need to be extracted from simple one-point functions to reconstruct specific aspects of correlation functions of the dual state with the best possible approximations.