## 3 Materials and Methods
### Performance Evaluation Metrics  
In this study, we employ beam prediction accuracy, precision, recall, specificity, and F1-score as performance metrics. These are commonly used evaluation metrics for ML classification problems [30; 31]. Also, we show the confusion matrices for additional visual representation of the hit and miss statistics, with respect to the true and predicted beam indices. The confusion matrix is a tabular representation that summarizes the performance of a classification model by recording the number of occurrences between the true/actual classes and the predicted classes [31]. In this paper, the confusion matrices are presented such that the columns represent the model predictions, while the rows display the true classes/ground truths. The metrics are calculated for each class/beam (i.e., \(\forall m\in\{1,2,\ldots,M\}\), and the results are averaged over the number of beams, \(M\). The metrics are mathematically defined in Equations (1)-(5), respectively. 1. Accuracy: This is a widely used metric for multi-class classification that can be directly derived from the confusion matrix using Equation (1). It represents the probability that the model's prediction is accurate (i.e., how much of the predictions match the ground truths) [31]. \[Accuracy=\sum_{m=1}^{M}\left(\frac{\text{TP}_{\text{m}}+\text{TN}_{\text{m}}} {\text{TP}_{\text{m}}+\text{TN}_{\text{m}}+\text{FP}_{\text{m}}+\text{FN}_{ \text{m}}}\right)\] (1) where TP is true positives, TN is true negatives, FP is false positives, and FN is false negatives. 2. Precision: This measures the model's ability to identify instances of a particular class correctly. It is the number of correctly classified positive samples (i.e., true positives) divided by the number of samples labeled by the system as positive, as given by Equation (2). It indicates how much we can trust the model when it predicts samples as positive. \[Precision=\frac{1}{M}\sum_{m=1}^{M}\left(\frac{\text{TP}_{\text{m}}}{\text{TP} _{\text{m}}+\text{FP}_{\text{m}}}\right)\] (2)
3. Recall: This measures the model's ability to identify all instances of a particular class. It is the number of the correctly classified positive samples divided by the number of positive samples in the data, as given by Equation (3). It measures the ability of themodel to find all the positive samples in the dataset [31]. Recall is also known as the true positive rate (TPR) or sensitivity. \[Recall=\frac{1}{M}\sum_{m=1}^{M}\left(\frac{\text{TP}_{\text{m}}}{\text{TP}_{ \text{m}}+\text{FN}_{\text{m}}}\right)\] (3)
4. Specificity: This measures the model's ability to identify negative instances of a particular class. It is the number of the correctly classified negative samples divided by the sum of the true negatives and false positives in the data, as given by Equation (4) [31]. Specificity is also known as the true negative rate (TNR) \[Specificity=\frac{1}{M}\sum_{m=1}^{M}\left(\frac{\text{TN}_{\text{m}}}{\text{ TN}_{\text{m}}+\text{FP}_{\text{m}}}\right)\] (4)
5. F1-score: This metric provides a comprehensive assessment of a classification model's performance, taking into account both the ability to correctly identify positive instances (precision) and the ability to capture all positive instances (recall). It aggregates the precision and recall measures under the concept of harmonic mean, and its formula can be interpreted as a weighted average between precision and recall [31]. F1-score is evaluated using Equation (5). The F1-score ranges from 0 to 1, where a score of 1 indicates perfect precision and recall while a score of 0 indicates poor performance. In practice, F1-score values closer to 1 are desirable, as they indicate a well-balanced trade-off between precision and recall.