## 4 Results
### Feature Selection  
The variables selection was based on the minimum redundancy maximum relevance procedure (mRMR) approximation method [39]. Consequently, it is required to first estimate the correlation between the numerical explanatory variables. The nonparametric Kolmogorov-Smirnov (K-S) test enables assessment of whether or not these predictors fit a normal distribution, therefore determining which further statistic ([PERSON]'s coefficient or Spearman's rho) should be used. Since all the features were significant in K-S except \"v\({}_{1f}\)\", only this variable did not fit a normal distribution. Consequently, it was decided to use Spearman's coefficient to estimate the correlation between the numerical variables (Figure 12). Since not all predictors fit a normal distribution, a binary logistic regression model was chosen to evaluate the correlation degree of the numerical predictors with the response variable \"Event\". Conditional backward recursive elimination showed that the variables \"t2\" (sig. < 0.001), \"d2\"(sig. < 0.001), \"v11\" (sig. = 0.024), \"v1f\" (sig. < 0.001), \"t1o\" (sig. = 0.008) and \"a1i\" (sig. < 0.001) were those with the highest degree of correlation with the response variable. To study the level of dependence of the categorical variables with each other and with the response variable, the chi-square test (\(\chi^{2}\)) was used (Table 2). The null hypothesis (H\({}_{0}\)) is that the variables are independent, while the alternative hypothesis (H\({}_{1}\)) is that there is a relationship between the pair of variables analyzed. In both the chi-square test of \"Interaction_type\" with \"Reaction_type\" and the corresponding test of \"Interaction_type\" and \"Event\", the count was greater than 20%, indicating the need to perform [PERSON]'s exact test for both comparisons. From the results obtained in the previous tests, it can be deduced that:  
* Of the numerical variables that are significant in the binary logistic regression model, \"d2\" and \"t2\" are those that maintain a higher level of correlation with the rest of the variables (with the exception of \"t0i\" and \"a1i\"). Therefore, the introduction of the variables \"d2\" and \"t2\" in the classification models is ruled out. * Given that the \"Reaction_type\" variable turns out to be the only significant variable in the chi-square test when evaluated with \"Event\", and, in turn, this test indicates that there is no independence with \"Interaction_type\", it was decided to introduce only \"Reaction_type\" to the classification models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Interaction\_Type** & **Reaction\_Type** & **Event** \\ \hline Interaction\_type & 1 & 24.381 (sig. < 0.001) & 0.177 (sig. = 1.000) \\ \hline Reaction\_type & 24.381 (sig. < 0.001) & 1 & 17.055 (sig. < 0.001) \\ \hline Event & 0.177 (sig. = 1.000) & 17.055 (sig. < 0.001) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation value between features and output variable, as well as the corresponding significance level, through the chi-square test and [PERSON]’s exact test. Figure 12: Correlation bubble plot of numerical variables in the database, using the Spearman’s rho statistic. Therefore, the explanatory variables introduced to the model are \"v\({}_{1i}\)\", \"v\({}_{1i}\)\", \"t\({}_{01}\)\", \"a\({}_{1i}\)\" and \"Reaction_type\". Due to the existence of a greater number of numerical features, the scatterplots of each pair of explanatory variables (grouped by categories of the response variable) were obtained from the _ggpairs_ function of the _ggplot2_ library in RStudio (Figure 13). This function also allowed us to obtain density plots, histograms, and whisker plots of the response variable. Regarding the density functions, these confirm that the variable \"v\({}_{1i}\)\" can fit a normal distribution, as confirmed by the K-S test. In the case of \"t\({}_{01}\)\" and \"a\({}_{1i}\)\", the density functions have a higher degree of skewness.