## 3 Materials and Methods
### Informative Variable Identifier
To do that, an importance measure was used: \(Imp_{l}\), which was calculated for each feature \(l\) as the absolute value of the average weight across replicates and divided by the square of the range of the 95% confidence interval of the resampled weights; that is,

\[Imp_{l}=\frac{|mean(\mathbf{W}_{l}^{*})|}{\left(\mathbf{w}_{l}^{h,*}-\mathbf{ w}_{l}^{h,*}\right)^{2}},l\in I \tag{14}\]

where \(\mathbf{w}_{l}^{h,*}\) is the value for upper interval, \(\mathbf{w}_{l}^{h,*}\) corresponds to the lower interval, and \(I\) is a set of informative features. When a disjoint group contains only two features, the highest importance determined the most relevant feature in the group. The other feature were taken as redundant versions of the former. For larger groups, there can be more than one relevant feature. To find them out, we explored the complete set of subgroups of features that we would get if we increased the weight correlation threshold used to build the initial disjoint group. Next, we defined the importance of a (sub)group of features \(G\) as the absolute value of the sum of the importance of the features in the group. As a result, groups or subgroups with sum of importance well above 1 were likely to include more than one relevant feature. On the contrary, not very informative subgroups with no relevant features were expected to have sum of importance well below 1. Subgroups with only one relevant feature were expected to get sum of importance close to 1. Therefore, for each initial disjoint group, we selected the subgroups configuration yielding the lowest sums of subgroup importance. Each of these subgroups contained one relevant feature and its redundant copy. Finally, the resulting groups and subgroups of features had to be ranked. We used a different importance measure which places more weight on the dispersion of the estimates of the coefficients of the features. Each feature importance was calculated and then normalized by dividing it by the maximum importance. The groups of features were ordered by taking into account the sum of their importances. The final ranking of features builds on this group ordering to show first the relevant features in each informative (sub)group, and then, following the same group-importance descending order, the redundant features. ### Methodology to Achieve Interpretability

In this work, we aimed to create a reliable, unbiased, and interpretable methodology to automatically measure CFD risk. In this sense, the proposed methodology involves three steps. The entire system flow of the proposed model is shown in Figure 1. This methodology is sequentially described step by step as follows. * Step 1: FS Extraction. Extract common informative features by applying the IVI algorithm. * Step 2: FS Filtration. * Step 3: Interpretability based on features' weights. The first step of this methodology consists of an FS stage that allows us to find the relevant features and to reduce the noise in the data models. As noted earlier, we use IVI [13], which is capable of identifying the most relevant features. The IVI algorithm introduced in the original work was implemented with CME as a weight generating method designed to be fast with the standard linear algorithms. In our methodology, we have expanded the weight generator using different classification algorithms. Our underlying rational to this approach is twofold. On the one hand, we aimed to obtain a more comprehensive view of the problem, due to the fact that each algorithm yields its own specific properties. On the other hand, the features that have been selected in multiple algorithms are more consistent. For this purpose, in this work we included SVM, LDA, LR, and GB in addition to CME. All these methods are linear. The algorithm to obtain the relevant features for each ML algorithms is shown as Algorithm 4.