## 2 Materials and Methods
### Analysis of Important Features
#### 3.4.2 Analysis Methods  
Understanding the relative importance of features within a model allows for better interpretation of results and can assist in the optimization and simplification of future models. Below are some standard methods used to evaluate feature importance.  
If a tree-based model is used, such as a Decision Tree or Random Forest, it is possible to directly obtain a ranking of features based on how these models make decisions. The degree to which a feature is used to partition data indicates its importance. Table 8 presents a quantitative breakdown of the most influential characteristics in the model. Firstly, \"Syntactic Structure\" stands out with an importance of 42%. This underlines how sentences are formulated, and their grammatical construction is predominant in the model's decisions. Second, with an extent of 35%, is \"Behavioral History,\" indicating that previous interactions and user behavior are essential to predict and adapt system responses. Finally, \"Demographic Data,\" which includes aspects such as the user's age, gender, or location, contributes 23% to the model's decision-making. Although it is less influential than the other two characteristics, it is still relevant.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Characteristic** & **Importance** \\ \hline Syntactic structure & 0.42 \\ Behavior history & 0.35 \\ Demographics & 0.23 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Feature importance table (Random Forest).  
For models such as logistic regression, the coefficients assigned to each characteristic can indicate their relevance in the prediction. Table 9 shows the coefficients associated with each characteristic in a logistic regression model and the respective values of the t statistic, which indicate the significance of each coefficient. \"Syntactic Structure\" has a positive coefficient of 2.45 and a t-statistic of 5.1. This suggests that as the salience of this feature increases, the model response is more likely to be positive, and its significance is high. On the other hand, \"Behavioral history\" presents a negative coefficient of \(-1.30\) with a t-statistic of 4.5, which implies that an increase in this aspect could decrease the probability of a positive response. Still, it remains a significant predictor in the model. Finally, \"Demographics\" shows a positive coefficient of 0.75 and a t-statistic of 3.0, indicating a positive relationship of moderate magnitude with the model response and moderate significance compared to the other two characteristics.  
In permutation techniques, it is possible to infer its relevance by randomly changing the order of a feature and measuring how much it affects the performance of the model. Table 10 reflects the effects on model accuracy after randomly permuting the values of each feature. This analysis method helps us understand the relative importance of features by observing how much model performance deteriorates by altering the original information. \"Syntactic structure\" shows a 7% decrease in accuracy after permutation, suggesting that it is a crucial feature for the correct functioning of the model. Any perturbation in the syntactic structure leads to a significant degradation in the model's predictive capacity.  
\"Behavior History\" has a 5% reduction in precision, pointing out its relevance, although to a lesser degree than syntactic structure. This implies that the user's past behavior is a valuable prediction indicator, and its alteration significantly affects the model's performance. Finally, the permutation of \"Demographics\" results in a more modest 2% decrease in accuracy. Although this feature impacts predictions, it is less critical than the other two. This analysis underscores the importance of syntactic structure in the model, followed by behavioral history. At the same time, demographics play a more secondary role in the model's overall accuracy.