  We explore the applications of machine learning techniques in relativistic
laser-plasma experiments beyond optimization purposes. We predict the beam
charge of electrons produced in a laser wakefield accelerator given the laser
wavefront change caused by a deformable mirror. Machine learning enables
feature analysis beyond merely searching for an optimal beam charge, showing
that specific aberrations in the laser wavefront are favored in generating
higher beam charges. Supervised learning models allow characterizing the
measured data quality as well as recognizing irreproducible data and potential
outliers. We also include virtual measurement errors in the experimental data
to examine the model robustness under these conditions. This work demonstrates
how machine learning methods can benefit data analysis and physics
interpretation in a highly nonlinear problem of relativistic laser-plasma
interaction.
