  Machine Learning (ML) inspired algorithms provide a flexible set of tools for
analyzing and forecasting chaotic dynamical systems. We here analyze the
performance of one algorithm for the prediction of extreme events in the
two-dimensional H\'enon map at the classical parameters. The task is to
determine whether a trajectory will exceed a threshold after a set number of
time steps into the future. This task has a geometric interpretation within the
dynamics of the H\'enon map, which we use to gauge the performance of the
neural networks that are used in this work. We analyze the dependence of the
success rate of the ML models on the prediction time $T$ , the number of
training samples $N_T$ and the size of the network $N_p$. We observe that in
order to maintain a certain accuracy, $N_T \propto exp(2 h T)$ and $N_p \propto
exp(hT)$, where $h$ is the topological entropy. Similar relations between the
intrinsic chaotic properties of the dynamics and ML parameters might be
observable in other systems as well.
