We compared FART to tree-based classifier models (XGBoost, Random Forest) which are considered strong baselines for classification tasks compared to more complex model architectures such as transformer models . This benchmarking relative to simpler, tree-based classifiers, which often perform exceptionally well with tabular data, is necessary to assess whether the added complexity of the transformer architecture is justified.