We present the discovery of a relationship between the maximum ratio of the flare flux (namely, 0.5-4 Ang to the 1-8 Ang flux) and non-flare background (namely, the 1-8 Ang background flux), which clearly separates flares into classes by peak flux level. We established this relationship based on an analysis of the Geostationary Operational Environmental Satellites (GOES) X-ray observations of ~ 50,000 X, M, C, and B flares derived from the NOAA/SWPC flares catalog. Employing a combination of machine learning techniques (K-nearest neighbors and nearest-centroid algorithms) we show a separation of the observed parameters for the different peak flaring energies. This analysis is validated by successfully predicting the flare classes for 100% of the X-class flares, 76% of the M-class flares, 80% of the C-class flares and 81% of the B-class flares for solar cycle 24, based on the training of the parametric extracts for solar flares in cycles 22-23.
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