## 3 Materials and Methods
### Informative Variable Identifier
We claim that it is possible to build robust, explanatory linear models that simultaneously meet the regulatory constraints and use the power of ML techniques. To do so, our work was twofold. First, we developed a synthetic dataset to define and fine-tune the models. Second, the successful models were later on applied to a real dataset to verify generalization and consistency. The main conclusions when analyzing the synthetic dataset are described hereafter. First, using the IVI model, we were able to systematically identify the features with informative value. Additionally, the use of a subset of the variables when applying the filters described in this paper improved the performance in terms of the computational efficiency by limiting the number of variables. We found that all noisy and redundant variables were consistently excluded from this extended method. Two different filtering procedures

Figure 10: Features’ weights. Graphical weights for the features selected in MIFF filter. Each color bar represents a different ML technique: GB, SVC, LDA, LR. were proposed, RFF and MIFF. The first one is much more restrictive and was the fastest way to reach a reasonable level of accuracy, but failed in the classification of a number of features. On the other hand, the second filter, although it had redundant variables, did not misclassify any noisy features. As the interpretations of linear models were proposed based on the final weights obtained, and considering that far lower contributions were found for these redundant features, the joint application of MIFF and weight evaluation could be considered as an efficient and accurate model, even in the cases when redundant features are identified. Based on these results, we conclude that formulation and classification using IVI, together with RFF and MIFF filtering, offers an automatic and efficient system that improves the generalization and prediction capabilities of CFD on synthetic datasets. The results of applying these methodologies to the German Credit dataset [5; 38] were in general terms consistent with the previous findings on the synthetic dataset. The results obtained suggested that the use of presented method not only improved the results (with a 4% accuracy increase compared to previous papers' results) but also enhances computational efficiency by reducing the number of features. From a methodological perspective, the applied model was confirmed to be valid, as accuracies for all method-characteristic models were homogeneous. Their lowerest values were close to 75% on average with a standard deviation of close to 2%. From a computational perspective, and considering the four different sub-sets of features evaluated, namely, all features, IVI features, MIFF, and RFF, the last two offered significant reductions in the number of variables, thereby significantly improving the computer's workload. For both, accuracy was high, although MIFF offered higher stability in the results across methods. Detailed analysis showed that RFF managed to incorporate effectively none but relevant variables, but missed in certain cases some of the relevant ones. MIFF managed in all cases to include them all, but also included some redundant ones. The integration of redundant variables in MIFF did not generate any lack of accuracy or stability in the predictive capacity, and they could be removed at a later stage, as their contributions (weights) steadily had far lower magnitudes compared to their peers' weights. On the contrary, in the RFF case, the rapid convergence due to the adjusted base of variables selected in this subset was not accompanied by the most stable behavior in the results, due to the absence of some significant variables due to the incorrect classification. Thus, there was an up to four percent reduction in precision compared to other the models.