## 1 Introduction  
Machine learning has become an important technology used in diverse sectors, transforming how data are analyzed and utilized [1, 2]. Examples of this can be found in fields such as healthcare, financial services, and cybersecurity, where the application of machine learning models has revolutionized the ways risks are predicted, detected, and mitigated [3, 4, 5]. The widespread application of both machine learning and deep learning techniques increases the need for new researchers to understand basic statistical principles [6]. This highlights the importance of correctly applying statistical methods and selecting appropriate evaluation metrics to assess model performance [6]. Metrics like precision, recall, and the F1-score originate from disciplines reliant on empirical evidence, including clinical trials, information retrieval, and behavioral research, and have been adapted to evaluate machine learning algorithms [7, 8]. In information retrieval, precision and recall measure how well a system retrieves relevant documents requested by the user, whereas in machine learning, relevant documents correspond to positive examples or positive class, and irrelevant documents correspond to negative examples or negative class [9, 10]. The consequences of mistakes in evaluating classifiers can be particularly severe in fields such as healthcare, where the costs associated with false negatives (FNs) and false positives (FPs) are significant [11, 12]. For instance, a false negative in a disease diagnosis could result in delaying treatment, while a false positive could lead to unnecessary treatments [11]. This underscores the importance of verifying that the metrics used to evaluate machine learning classifiers are appropriate [1]. [PERSON] explores this and affirmsthat diagnosing a disease is a classification task evaluated using performance metrics such as accuracy, precision, recall, and F1-score [13; 14]. These four evaluation metrics are commonly used to assess classifiers not only in the medical field but also in areas such as sentiment and emotion analysis. However, researchers often select only one metric, with accuracy being the preferred choice [11; 14; 15; 16]. Nevertheless, given the cost sensitivity of FN and FP, it is important to consider metrics like precision and recall, which provide a more nuanced evaluation of a classifier's performance. The presence of class imbalance in training datasets can result in the majority group being overclassified while examples from the minority class are more frequently misclassified [17; 18]. When classes are imbalanced, accuracy may be misleading, presenting high scores that do not represent reliable performance [14; 17]. Therefore, when working with an imbalanced dataset, it is recommended to use multiple performance metrics [14; 18]. Still, criticism persists regarding the use of recall, precision, and the F1-score in text classification, as they overlook the accurate classification of true-negative examples and fail to account for performance that could result from chance [7; 19; 20]. Sentiment and emotion analyses frequently encounter the challenge of class imbalance, representing a key obstacle to improving sentiment and emotion classifiers [21; 22]. The challenge of class imbalance in emotion analysis datasets can lead to models that perform well on majority classes but poorly on minority ones, as some emotions are significantly more represented than others [21; 23; 24]. This issue is evident with the use of accuracy as an evaluation metric when there is class imbalance, as classifiers tend to give more importance to the majority class, complicating the classifier's ability to perform effectively on the minority class [23]. Metrics like accuracy and the micro and macro F1-score are recommended for evaluating the performance of multilabel classifiers [6]. The recent literature shows that accuracy and the F1-score are the most used performance metrics for multilabel classifiers [6; 25].