## 4 Materials and Methods
### Feature Selection Approach
#### 4.2.1 Big Five Classification Predictions as Features  
Our first hypothesis is based on the idea that the difference in the domain between personality models can ultimately impede the prediction results. While Enneagram types are represented using a whole number on a scale from 1 to 9, the MBTI types can be described using a binary value of either 0 or 1 in order to aptly represent each of the four dichotomies. On the other hand, Big Five personality traits are labelled using a positive  
Figure 7: Heatmap indicating linear correlation between labels for the Big Five traits and MBTI types present in the PANDORA dataset [7].  
number within the range of 0 to 100, with some labels even being represented as a single precision decimal number. It is possible to minimize these differences by introducing an additional step to the prediction process, which would treat the task as a classification problem rather than a regression one.  
To convert the Big Five labels from continuous values into discrete ones, we applied binning as described by [PERSON] et al. [76], with slight modifications. In their work, the authors used two different techniques in order to separate Big Five traits into binary classes--(1) utilizing the mean value of the particular personality trait and (2) by using the first and third quartiles of the distribution as class delimiters and discarding all values between the them. As the data distributions of the PANDORA dataset and the data used by [PERSON] et al. [76] differ, we introduced slight adjustments to the approaches they used.  
When it comes to technique (1), instead of relying on the mean value, we decided to use the median point of the personality trait distribution as a separator between classes. The reasoning behind this is that the median values tend to be more resilient to skewed data distributions, thus, making it a better fit for the PANDORA dataset (Figure 6). For technique (2), we decided against discarding any non-extreme value, and instead binned the Big Five traits into three classes rather than two. In doing so, we prevented any loss of information since the Big Five personality labels present in the PANDORA dataset are relatively smaller in size when compared to MBTI (Table 2).  
Despite the recent success of different deep-learning approaches in predicting Big Five traits as classes [64; 100; 101], we decided to use the same regression algorithm as in the case of predicting MBTI and Enneagram types, so to allow for better comparability. The features used for this task include \(n\)-grams and MBTI/Enneagram predictions as described in [PERSON] et al. [7]. These predictions were then later used for the regression model with Figure 8 illustrating the steps taken in predicting the continuous values for the Big Five personality traits.  
Figure 8: Illustration of the model stack after introducing the Big Five classification predictions.  
[PERSON] highlights the added logistic regression model that predicts Big Five traits as one of either two or three classes, depending on if the median or quartile values are used as separators between classes. The newly added model acts as a weak learner in the model stack.