  Unfolding in high energy physics represents the correction of measured
spectra in data for the finite detector efficiency, acceptance, and resolution
from the detector to particle level. Compared to other commonly used unfolding
methods, recent machine learning approaches provide unfolding on an
event-by-event basis using all the information from the collision similarly to
the face recognition problem and allows to unfold spectra in a continuous way
(independent of binning choice) the simultaneous unfolding of a large number of
variables and thus can cover a wider region of the features that effect
detector response. This study focuses on a simple comparison of commonly used
methods in RooUnfold package to the machine learning package Omnifold.
