Table 6 Summary of performance evaluation metrics.
From: Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM
Metric | Definition | Purpose in emotion classification |
|---|---|---|
Accuracy | Proportion of total correct predictions (TP + TN)/(TP + TN + FP + FN) | Measures overall performance across all classes |
Precision | TP/(TP + FP) | Measures correctness of positive predictions |
Recall (sensitivity) | TP/(TP + FN) | Indicates how well the model detects actual positives (emotions) |
F1-score | Harmonic mean of precision and recall | Balances false positives and false negatives |
Confusion matrix | Matrix showing predicted versus actual class counts | Visual representation of model’s classification accuracy by class |
AUC-ROC | Area under the receiver operating characteristic curve | Evaluates the ability to distinguish between emotion classes |
