  We use ensemble machine learning algorithms to study the evolution of
magnetic fields in magnetohydrodynamic (MHD) turbulence that is helically
forced. We perform direct numerical simulations of helically forced turbulence
using mean field formalism, with electromotive force (EMF) modeled both as a
linear and non-linear function of the mean magnetic field and current density.
The form of the EMF is determined using regularized linear regression and
random forests. We also compare various analytical models to the data using
Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling. Our results
demonstrate that linear regression is largely successful at predicting the EMF
and the use of more sophisticated algorithms (random forests, MCMC) do not lead
to significant improvement in the fits. We conclude that the data we are
looking at is effectively low dimensional and essentially linear. Finally, to
encourage further exploration by the community, we provide all of our
simulation data and analysis scripts as open source IPython notebooks.
