## 5 Results  
In Section 4, we detailed several approaches that rely on different features and algorithms. Due to the large quantity of these approaches and for the sake of providing a detailed and structured comparison between the results of our experiments, we separate this chapter into two subsections, with Figure 9 providing a general overview of the flow of our experiments. In the first subsection, we focus on listing the results achieved through the feature selection approach, while the second part reports the results for each of the different regression algorithms applied. Additionally, we provide a more streamlined and concise overview of our findings in (Section 6). Figure 9: Simplified illustration of the pipeline behind our method. The green rectangle depicts feature selection approaches, while the red one highlights the model selection step. Before detailing the results of our experiments, we briefly go over the results of the baseline approach as well as the evaluation criteria. [PERSON] et al. [7] were the first to test the hypothesis of using MBTI and Enneagram predictions to successfully predict Big Five labels. In their experiments, they used several different feature sets, such as \(n\)-grams and MBTI/Enneagram predictions that were the result of logistic regression models. These features were later combined with regression models to acquire the predicted Big Five labels. In Table 4, we present the correlations between the predicted MBTI types and Big Five traits with the ground truth labels present in the PANDORA dataset as reported by [PERSON] et al. [7]. Their best performing model was a L2 regularized linear regression model that used a combination of \(n\)-grams and predictions of the MBTI/Enneagram labels as features, yielding the best results for nearly all of the Big Five personality traits. The only exception was the _Openness_ trait, which demonstrated better performances when the same regression model was used, only without the MBTI/Enneagram predictions in the feature set. The metric used to evaluate the performance of these models is known as the Pearson correlation coefficient [113]. The results of their experiments are reported in Table 5 for the sake of providing a reference when comparing the results with those achieved through experiments. While [PERSON] et al. chose the Pearson correlation coefficient for their evaluation metric, it is essential to note that various metrics have been used in personality computing to evaluate the performances of different models [6]. When focusing strictly on regression problems, the Pearson \(\mathbf{r}\) correlation is not an uncommon metric; however, in the cases where the data do not follow a normal distribution, Spearman's rank correlation coefficient might be a slightly better choice. Additionally, it was proposed by [PERSON] et al. [19] to use more than a single metric when evaluating personality-recognition approaches. For example, using the mean squared error (MSE) in addition to correlation metric would reveal the absolute difference between predictions and how the model follows the trend. In our work, we chose to focus on exploring the effectiveness of different features and algorithms for personality prediction, and how they capture the relationship between multiple models. For the sake of allowing direct comparisons with previous work, we decided to use only a single metric that has been used, which is the Pearson \(\mathbf{r}\) correlation coefficient. \begin{table}
\begin{tabular}{l|r r r r r} \hline  & **Openness** & **Conscient.** & **Extroversion** & **Agreeableness** & **Neuroticism** \\ \hline Pearson corr. & 0.250 & **0.273** & 0.387 & **0.270** & **0.283** \\ \hline \end{tabular}
\end{table} 
Table 4: The results of the baseline approach. The Pearson correlation coefficient scores were adapted from the work of [PERSON] et al. [7] CC-BY-NC.