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

Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence