## 3 Experiments and Results
### Feature Evaluation Capabilities  
Although any variable could be considered to be additional information, and to some extent could add value to a sample base, large number of different variables could also create difficulties to ML convergence. In one side, the number of variables should always remain in several orders of magnitude lower than the number of samples. Second, the excess of information, duplicated, or just uncorrelated with the final goal, could limit the convergence capabilities, lead to local minimums, lead to overfitting, and/or increase the computational cost. Therefore, it is often appropriate, and even necessary, to follow a certain strategy to make sure that we are considering the right and most relevant features from our datasets. Therefore, one key goal in ML implementation is to select the key features to improve the precision performance and to provide fast and effective tools, which at the very same time offers a sensitive scenario. For additional information and better understanding see [41].  
Although several the statistical learning methods presented in this work exhibited to be robust against the incorporation of variables, as the SVR, it is not always easy to establish an automatic or implicit procedure for select the features that can drive better results. Hence, in this study, a good number of experiments were carried out with different configurations that characterize and evaluate the features in the input space for the proper fine-tuning of the models. As mentioned earlier, in this study we have considered the operational normalization of the input variables as the base scenario for the analysis. However, to verify the results in other possible input space, we also reproduced the analysis simultaneously by applying the operational and statistical normalization as defined in the companion paper. Table 4 shows the results obtained when only endogenous variables were considered, and with endogenous and exogenous variables jointly incorporated. A comparison of scenarios revealed that the findings were consistent with those expressed earlier. A slight improvement was found when exogenous variables were included, but no significant differences were identified after statistical normalization.  
An equivalent analysis was carried out to assess the incremental valuation aptitudes of the complex estimated variables that are usually incorporated in valuations by analysts, in particular the NPVs defined and calculated previously described in the companion paper. The results indicate that the incorporation of these compounded estimates, subordinated to the main ones already present in the input space, did not add predictive capacity to the model, as showed in Table 4 (Extended Features column).  
Once confirmed that neither the incorporation of double normalization nor the addition of complex, generally accepted, subordinated variables incremented the classification or regression capabilities of the implemented methods, we evaluated subsets of variables. The aim was to define a computationally low-complexity model by choosing the features that consistently showed greater relevance when decision trees were evaluated, although not in all realization and methods in the same order: first year FCF, net debt, GDP and RIR. An illustrative example of this relevance is presented in Figure 2 for only endogenous (a), and for both endogenous and exogenous (b) variables. The results of this simulation, presented in the last column of Table 4 (Key Features column), showed that the exclusive use of the four most significant variables either slightly worsened or matched the results obtained in the base case scenario considering endogenous and exogenous operationally normalized variables.