  The IceCube Observatory at the South Pole allows the measurement of the
diffuse neutrino flux. The assumption of specific flux parametrizations limits
the range of spectral shapes. Given the increasing statistics of the data
recorded, model-independent unfolding approaches can overcome these
limitations. In this contribution, a model-independent approach to estimate the
muon neutrino flux between 125 GeV and 2.5 PeV is presented. In order to
extract muon neutrinos from the data taken by the detector, a
machine-learning-based method is employed. This yields an efficient muon
neutrino sample with a purity of over 99 %. The spectrum is estimated by a
Likelihood-based unfolding technique involving a novel binning scheme using a
decision tree on three years of IceCube data. This measurement provides the
first model-independent muon neutrino spectrum for multiple years in this
energy regime.
