  Whilst the most dynamic solar active regions (ARs) are known to flare
frequently, predicting the occurrence of individual flares and their magnitude,
is very much a developing field with strong potentials for machine learning
applications.
  The present work is based on a method which is developed to define numerical
measures of the mixed states of ARs with opposite polarities. The method yields
compelling evidence for the assumed connection between the level of mixed
states of a given AR and the level of the solar eruptive probability of this AR
by employing two morphological parameters: (i) the separation parameter
$S_{l-f}$ and (ii) the sum of the horizontal magnetic gradient $G_{S}$.
  In this work, we study the efficiency of $S_{l-f}$ and $G_{S}$ as flare
predictors on a representative sample of ARs, based on the SOHO/MDI-Debrecen
Data (SDD) and the SDO/HMI - Debrecen Data (HMIDD) sunspot catalogues. In
particular, we investigate about 1000 ARs in order to test and validate the
joint prediction capabilities of the two morphological parameters by applying
the logistic regression machine learning method. Here, we confirm that the two
parameters with their threshold values are, when applied together, good
complementary predictors. Furthermore, the prediction probability of these
predictor parameters is given at least 70\% a day before.

Solar and Stellar Astrophysics