In this work, we have evaluated the ability of DFT/MRCI(2) to describe seams of conical intersections using a GPR surrogate approach. To obviate the need to learn adiabatic surfaces, which exhibit cusps at points of degeneracy, we instead learned the coordinate dependant coefficients of the characteristic polynomial of the potential matrix, which are smooth functions of nuclear coordinates. We found that the explicit inclusion and optimization of a whitenoise kernel allows for an intuitive framework to learn smooth surrogate DFT/MRCI(2) surfaces that are otherwise discontinuous due to the selected CI aspects of the method. Using the GPR surrogate characteristic polynomial coefficients, we optimized the MECIs of ethylene, butadiene and fulvene and computed for the first time the corresponding branching space and topography using DFT/MRCI (2). The asymmetry of the double cones at the optimized MECIs agree well with ab initio MRCI, but the tilt parameters are smaller in magnitude, indicating that DFT/MRCI(2) preferentially gives vertical double-cones compared to ab initio MRCI for each of the molecules studied. The difference in the optimized hyperparameters of the average energy surrogates, combined with the good agreement between the branching spaces, shows that the difference in the tilt parameters is a result of the learned average energy surface.