• Using the generated data to train and test novel functionals for MC-NCFT, representing a continuation of our initial work that used carbene singlet-triplet excitation energies to train machine-learned functionals. • Improving the active space selection scheme. Our finding that error thresholds can be used to determine the fraction of poor wave functions in the calculated excitation energies can be used as a measure to benchmark the effectiveness of different active space selection schemes.