  Solar flares originate from magnetically active regions but not all solar
active regions give rise to a flare. Therefore, the challenge of solar flare
prediction benefits by an intelligent computational analysis of physics-based
properties extracted from active region observables, most commonly
line-of-sight or vector magnetograms of the active-region photosphere. For the
purpose of flare forecasting, this study utilizes an unprecedented 171
flare-predictive active region properties, mainly inferred by the Helioseismic
and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the
course of the European Union Horizon 2020 FLARECAST project. Using two
different supervised machine learning methods that allow feature ranking as a
function of predictive capability, we show that: i) an objective training and
testing process is paramount for the performance of every supervised machine
learning method; ii) most properties include overlapping information and are
therefore highly redundant for flare prediction; iii) solar flare prediction is
still - and will likely remain - a predominantly probabilistic challenge.
