Model selection for fungal laccase activity: shallow learners versus neural networks under nested cross-validation

Due to their catalytic properties, fungal laccases are used in various technological fields, from bioremediation to biofuel production. Here, we revisited the model selection for extracellular laccase activity in Trametes villosa using a central composite design (CCD) with factors including temperature, initial pH, and inoculum volume. To address concerns about overfitting and evaluation leakage in small-N RSM studies, we adopted a strict nested cross-validation (CV) protocol (outer 5-fold for generalization and inner 3-fold for tuning) and compared three shallow learners: quadratic ridge regression, RBF-kernel support-vector regression (SVR), and random forests, against two compact multilayer perceptrons (MLPs). To preserve physical plausibility, we modeled the response as log(y + ε) with ε = 0.1, and reported RMSE and R² from the outer 5-fold CV on the back-transformed scale. Across outer folds, SVR (RBF) achieved the lowest RMSE with the smallest fold-to-fold variance; the random forests were competitive; MLPs were more variable, and the ridge underfitted. SHAP analyses for the best model highlighted temperature as the dominant driver, with pH and inoculum showing secondary, non-monotonic contributions. Our results indicate that shallow nonlinear methods generalize best on small RSM datasets and should be preferred for early-stage process optimization.