## 7 Future Translational Applications of the U-Smile Method
### Integration with ML Pipelines  
In applied ML workflows, the U-smile method can be a post hoc tool for evaluating variable usefulness and guiding model refinement. Embedded in decision-support systems or automated VS pipelines, it can provide complementary insights to techniques of explainable ML, such as SHapley Additive exPlanations (SHAP) or local interpretable model-agnostic explanations (LIME) [50; 51; 52]. As it is conceptually compatible with any  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Limitations of the U-Smile Method** & **Direction for Further Methodological Development** \\ \hline \hline \begin{tabular}{c} The U-smile method has been developed and validated on \\ logistic regression models \\ \end{tabular} & \begin{tabular}{c} Apply the U-smile method to non-linear or non-parametric \\ models, such as decision trees, random forests, or neural \\ networks as the U-smile method is conceptually compatible to \\ any model that produces probability estimates and does not \\ require any model assumptions or affect model training \\ \end{tabular} \\ \hline \begin{tabular}{c} The U-smile method has been developed and validated in the \\ context of nested models that differ by a single variable as this \\ setup reflects many applied use cases (particularly in biomarker, \\ clinical and epidemiological research) \\ \end{tabular} & \begin{tabular}{c} Apply the U-smile method to compare models differing by \\ multiple variables and non-nested models, including models \\ with partially overlapping sets of predictors \\ \end{tabular} \\ \hline \begin{tabular}{c} The U-smile method is currently applied post hoc after \\ model training \\ \end{tabular} & \begin{tabular}{c} Integrate the U-smile method with regularized frameworks like \\ LASSO, ridge or elastic net, automated VS algorithms and \\ stepwise regression \\ \end{tabular} \\ \hline \begin{tabular}{c} The U-smile method has been developed and validated for BC \\ tasks only \\ \end{tabular} & \begin{tabular}{c} Adapt and validate the U-smile method for multiclass \\ classification and time-to-event (survival) outcomes \\ \end{tabular} \\ \hline \begin{tabular}{c} Application of the U-smile method to real-time data may be \\ computationally intensive in very large datasets or \\ high-throughput environments \\ \end{tabular} & \begin{tabular}{c} Optimize algorithmic efficiency through parallel computing \\ approaches or approximate methods \\ \end{tabular} \\ \hline \
\begin{tabular}{c} The U-smile method has been evaluated on training and test \\ datasets comprising 300 and 100 observations, respectively \\ \end{tabular} &
\begin{tabular}{c} Investigate the effect of dataset size on the variability of the \\ BA-RB-I coefficients \\ \end{tabular} \\ \hline \hline \end{tabular}  
BC—binary classification; LASSO—least absolute shrinkage and selection operator; VS—variable selection.  
\end{table}
Table 2: Limitations of the U-smile method and directions for further methodological development.  
ML model that returns probability estimates, the U-smile method can be integrated in numerous algorithms, such as decision trees, random forests, or neural networks.