Self-prediction test for observation using three features: M_⋆, r_1/2, and σ with the balanced-counts training sample. The training–test sample has been re-scaled to ensure an equal number of entries across all samples through random selection, aligning with the less populated class (DynPop-nETGs). The training set consists of 80% of the randomly selected subsample (800 entries), while the remaining 20% (200 entries) is allocated for testing. The Fornax sample is too small, with fewer than 20 entries, making it impossible to conduct the self-prediction test.