  In this study, we aim to identify the most effective machine learning model
for accurately classifying Myers-Briggs Type Indicator (MBTI) types from Reddit
posts and a Kaggle data set. We apply multi-label classification using the
Binary Relevance method. We use Explainable Artificial Intelligence (XAI)
approach to highlight the transparency and understandability of the process and
result. To achieve this, we experiment with glass-box learning models, i.e.
models designed for simplicity, transparency, and interpretability. We selected
k-Nearest Neighbour, Multinomial Naive Bayes, and Logistic Regression for the
glass-box models. We show that Multinomial Naive Bayes and k-Nearest Neighbour
perform better if classes with Observer (S) traits are excluded, whereas
Logistic Regression obtains its best results when all classes have > 550
entries.
