The MFD value provides a quantitative representation of the model's capacity to differentiate between the ground-truth dataset comprising solely of positive samples and the query dataset containing a significant proportion of true negatives. A higher value of the MFD indicates an enhanced ability of the model to identify instances dissimilar to the known positive ground-truth pattern. Following the extensive evaluation and comparison of trained models constructed using various feature representations and algorithms by implementing the MFD approach, we highlight the importance of a pre-processing step to keep the number of features of each component at the same order of magnitude when training models lacking feature compression capabilities. Conversely, for deep learning-based models capable of learning feature importance, the performance does not improve by applying the feature compression pre-processing step but rather by providing more relevant feature information.