## 2 Materials and Methods
### Evaluation Metrics  
Throughout Section 4, we will report experimental results using the macro-averaged F1 metric (also known as maF1) as a single number summary. This is the standard metric for reporting multi-label classification results in the field (see Section 1.3), especially with imbalanced test sets. In this section, we provide a brief explanation of this metric and the other metrics used: precision and recall. Multi-label classification is a type of classification problem where each instance can be assigned to multiple classes simultaneously. In contrast to binary classification, where an instance is assigned to either one of two classes, multiclass classification, where each instance is assigned to exactly one class out of a set of mutually exclusive classes. In multi-label classification, each class label can be independently predicted, and an instance can belong to none, one, or multiple labels. In the context of multi-label classification, the terms True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN) are defined based on the prediction and ground truth of each class separately. * True Positives (TP): For a specific label, TP represents the instances where the model correctly predicts the presence of that label, and the ground truth also indicates the presence of that label. It is the count of instances that are correctly identified as positive for a particular label;
* False Positives (FP): For a specific label, FP occurs when the model predicts the presence of that label, but the ground truth indicates the absence of that label. It is the count of instances that are incorrectly classified as positive for a particular label;
* True Negatives (TN): For a specific label, TN represents the instances where the model correctly predicts the absence of that label, and the ground truth also indicates the absence of that label. It is the count of instances that are correctly identified as negative for a particular label;
* False Negatives (FN): For a specific label, FN happens when the model predicts the absence of that class, but the ground truth indicates the presence of that label. It is the count of instances that are incorrectly classified as negative for a particular label. Rather than directly reporting these raw counts, it is common to report precision, recall, and F1, which provide a more comprehensive measure of the model's performance as they take into account proportions rather than raw counts. * Precision measures the proportion of true positive predictions out of the total positive predictions (Equation (1)). It focuses on the correctness of positive predictions. A high precision indicates a low rate of false positives;
* Recall measures the proportion of true positive predictions out of the total positive instances in the dataset (Equation (2)). It focuses on capturing all positive instances without missing any. A high recall indicates a low rate of false negatives;
* F1 score is the harmonic mean of precision and recall. It provides a balanced measure that combines both precision and recall into a single metric (Equation (3)). It is commonly used when both precision and recall are equally important, providing an overall measure of the model's performance. \[precision = \frac{TP}{TP+FP} \tag{1}\] \[recall = \frac{TP}{TP+FN}\] (2) \[F_{1} = 2\frac{precision\cdot recall}{precision+recall} \tag{3}\]Accuracy is calculated as the ratio of the total number of correct predictions (both true positives and true negatives) to the total number of instances in the dataset (Equation (4)). In datasets, where the number of negative instances is much higher than the number of positive instances, the accuracy is usually misleading. A classifier that always predicts the majority class (negative) would achieve high accuracy due to the imbalance, even if it fails to correctly identify positive instances. In multi-label datasets with many labels, it is common for every label to have many more negatives than positives.