## 3 Materials and Methods
### Informative Variable Identifier
Results for the German credit dataset with the IVI technique and all the ML algorithms are presented in Figure 6. A number of features were unfailingly identified as relevant for all the ML algorithm, following the same pattern observed with the synthetic dataset and showing consistency with previously described results in terms of these repeated informative features. Equivalently to prior descriptive analysis, features were classified as RFF if they had been selected in all the ML algorithms used, and MIFF if they had been selected by at least in two of them. Four experiments were guided using one hundred epochs of bootstrap tests in order to evaluate the statistical significance. Results in Table 2 show systematically an almost insignificant standard deviation, with independence from the dataset. The method-characteristic paired models show high accuracy in all cases: the values were close to 75% in all cases. Regarding the methods, they all showed similar values for the different sets of variables, except the SVC method, which presented a drop of 4% with respect to the rest for the set of RFF variables. Singularly, this same method (SVC) offered the best result with the remaining feature set, by systematically surpassing the rest of the methods and reaching a maximum of 76.63% with the MIFF filter. On the other hand, lower results were steadily found, although still strong, with the GB method. Accuracy was worse by more than 1% for the feature sets at best, with the sole exception of the RFF, with which SVC presented a decline even larger. From a feature-set perspective, the results were very homogeneous among the methods, and singularly better in the case of MIFF, due to precision figures over 75.10% in all cases. On the contrary, the RFF filter showed not only uneven behavior across methods, but also consistently poorer results. The results shown here correlate highly with those of the analysis carried out with the synthetic dataset, thereby validating the hypotheses formulated during the exercise. Figure 5: Features’ weights. Graphical weights for the features selected by the MIFF filter. Each color bar represents a different ML technique: GB, SVC, LDA, LR. All the ML algorithms using MIFF showed higher accuracy using the IVI features. The only exception was LR, which had a slight reduction in accuracy. Those results are consistent with those previously obtained, and again, FS using MIFF improved the training procedure in terms of computational efficiency, by reducing the number of features needed to reach higher accuracy, and thus confirming empirically the hypothesis settled with the synthetic dataset. Equivalent representations appeared earlier for all evaluated models and available features. They are depicted in Figures 7 and 8. In particular, in Figure 7 we see the parallel and comparative processes followed in the IVI algorithm and the RFF filtered features; and in Figure 8, we also see the corresponding analysis for MIFF. It can be observed in figure that accuracy systematically increased in all cases when relevant features were added toward the maximum value. After that, the accuracy remained stable, despite the redundant and noisy features being added. Furthermore, when MIFF and RFF filters were used, we reduced the number of features to reach the maximum accuracy against the standard IVI approach. The best results were obtained using the MIFF filter. Figure 7 show slightly lower accuracy when compared to IVI. As we mentioned earlier, this is related to the extremely restricted set of candidates allowed to become relevant features when using this filtering technique.