## 3 Materials and Methods
### Informative Variable Identifier
Figure 10 illustrates the coefficients corresponding to the MIFF features. The representation illustrates the existence of large-contribution variables, over \(\pm 1.0\) (over_draft_<0, credit_usage, purpose_new_car, employment, other_parties_guarantor), and the large-contribution ones over \(\pm 0.5\) (credit_history_other_existing_credits, average_credit_balance_<100, location purpose_education), and a small proportion of variables with a significantly lower contribution less than 0.5 (purpose_rent, current_balance, credit_history_no_credits, foreign_worker). This last group very likely corresponds to the redundant variables that were incorrectly classified, and therefore, subjected to be excluded at a later stage. It is necessary to point out at this point the identification of 3 variables outstanding the general classification with contributions 50% higher than their peers. In particular, credit_usage was the most relevant feature for all algorithms, and the second most relevant was over_draft_<0. These results are consistent with literature [36; 37] and with the companion paper [12]. This special subset should be conveniently analyzed separately from an interpretability perspective, given this remarkable behavior: it is not only more important than the rest, but was consistently reproduced in every method. We should mentioned here the special situation of the GB method. Although significance apparently offered proportional matching with the rest of the methods, its magnitudes are far lower than those of the rest of the methods, following its behavior in the synthetic scenario. Figure 9: Graphical results for accuracy through incorporation of the relevant features in sequential order for RFF and MIFF. We can see number of features in the \(x\)-axis. Along the \(y\)-axis are the different ML algorithms (GB, SVC, LDA, and LR). The accuracy is along the \(z\)-axis. For each ML algorithm, we present the evolution of features selected by the MIFF filter as a continuous line and the features selected by the RFF filter as a dashed line. ## 5 Discussion and Conclusions

In this paper, we elaborated on the possibility of applying the almost ubiquitous current ML techniques to CFD. One of the main drawbacks of these technologies is that even though they are extremely effective and powerful in almost all disciplines, they are mostly black boxes: it is virtually impossible to decode the way the variables are treated internally. This last statement is intrinsically incompatible with regulations issued by administrative bodies, as whatever tool is used should be compliant with non-discriminatory rules and transparency. In this work, we proposed a novel methodology to address the mentioned drawbacks when applying ML to the CFD problem, which uses state-of-the-art algorithms capable of quantifying the information of the variables and their relationships. This approach offers a new method for interpretability to cope with this multifaceted dilemma. In this paper, we presented an intensive analysis of a number of statistical learning techniques (GB, SVR, LR, and LDA), together with new feature selection procedures, applied both on a synthetic dataset for development, and on a real dataset for validation. As a general conclusion of all the experiments, we can say that it is possible to develop an ML model supporting the novel feature selection techniques presented in this paper. The result will not only provide the detection of CFD, but also at the same time allows one to visualize the contribution of each feature in the decision process, thereby offering the necessary interpretability of the model and the results. To deal with the complex dichotomy of machine learning tools and interpretability, we elaborated on informative features and calculated their contributions to the decision process, leaving aside black boxes and minimizing potential biases by using state-of-the-art ML techniques.