1. The problem is to understand the evaluation metrics used to assess model performance in classification tasks. 2. Accuracy measures overall correctness: $$\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}$$ where TP = True Positives, TN = True Negatives, FP = False Positives, FN = False Negatives. 3. Precision measures how many predicted positives are correct: $$\text{Precision} = \frac{TP}{TP + FP}$$ 4. Recall measures how many actual positives are correctly predicted: $$\text{Recall} = \frac{TP}{TP + FN}$$ 5. F1-Score balances precision and recall: $$F1 = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}$$ 6. These metrics together ensure a reliable and comprehensive evaluation of a model's classification capability, especially useful in tasks like classifying motor states.