# Verse1-Chorus-Verse2 Structure: A Stacked Ensemble Approach for Enhanced Music Emotion Recognition
## 3 Results and Discussion
### Performance of Basic Stacked Ensemble Model
#### 3.1.1 Performance of Basic Stacked Ensemble for Verse 1 Dataset  
Figure 6 displays the performance metrics of the stacking method for the Verse1 dataset, presenting three different configurations indicated by the minimum document frequency (MinDF) settings of 5, 10, and 30. The metrics, as mentioned above, include accuracy, precision, recall, and F1 score--all crucial for evaluating a model's performance in classification tasks.  
The highest accuracy is observed with MinDF set to 5 (46.25%), declining as the MinDF increases. This suggests that including terms appearing in at least five documents enhances model performance. Lowering the MinDF threshold allows the model to capture more diverse and specific features from the dataset, thereby improving accuracy.  
Highest precision is attained at the highest MinDF of 30 (48.72%), indicating that the model is more precise at this threshold. Focusing on terms more prevalent across the dataset potentially leads to more confident predictions, reducing false positives. Consequently, precision is optimized when the model prioritizes terms with higher frequencies.  
Recall peaks with MinDF set to 5 (46.69%), akin to accuracy, suggesting that a lower threshold includes terms crucial for identifying positive instances accurately. Lowering the MinDF threshold enables the model to capture a broader range of terms, enhancing recall by effectively identifying more true positive instances.  
The F1 Score mirrors trends observed in accuracy and recall, with the highest value at MinDF = 5 (47.24%). Achieving a balance between false positives and false negatives is optimal at this setting, as indicated by the harmonic mean of precision and recall. Lower  
Figure 6: Verse 1 Performance Evaluation Results.  
MinDF thresholds allow the model to maintain this balance by capturing a broader range of significant terms, leading to improved overall performance in classifying emotional states.  
Figure 6 illustrates a clear trade-off between precision and recall. As precision increases with higher MinDF settings, recall and accuracy decrease, highlighting the need to strike a balance based on the desired outcome of the model's application. These findings offer valuable guidance for model tuning. For applications prioritizing recall, such as avoiding missing positive cases, a lower MinDF might be favored. Conversely, in scenarios where precision is paramount, such as when a false positive carries significant consequence, a higher MinDF could be more appropriate.  
The impact of MinDF on model performance underscores its role as a feature selection technique in text processing, influencing which terms are included as features in the model. These results underscore the importance of considering the dataset and the problem space. If the dataset contains many rare but informative terms, opting for a lower MinDF threshold could capture this valuable information. Conversely, a higher MinDF threshold might be advantageous in focusing on more commonly occurring terms, potentially enhancing model performance in certain contexts. Therefore, understanding the dataset's characteristics and the specific requirements of the problem space is crucial when determining the optimal MinDF setting for model training and application.