  The Mpemba Effect can be studied with Markovian dynamics in a non-equilibrium
thermodynamics framework. The Markovian Mpemba Effect can be observed in a
variety of systems including the Ising model. We demonstrate that the Markovian
Mpemba Effect can be predicted in the Ising model with several machine learning
methods: the decision tree algorithm, neural networks, linear regression, and
non-linear regression with the LASSO method. The positive and negative accuracy
of these methods are compared. Additionally, we find that machine learning
methods can be used to accurately extrapolate to data outside the range which
they were trained. Neural Networks can even predict the existence of the Mpemba
Effect when they are trained only on data in which the Mpemba Effect does not
occur. This indicates that information about which coefficients result in the
Mpemba Effect is contained in coefficients where the results does not occur.
Furthermore, neural networks can predict that the Mpemba effect does not occur
for positive $J$, corresponding to the ferromagnetic ising model even when they
are only trained on negative $J$, corresponding to the anti-ferromagnetic ising
model. All of these results demonstrate that the Mpemba Effect can be predicted
in complex, computationally expensive systems, without explicit calculations of
the eigenvectors.
