## Heatmap: Classification Confusion Matrix ### Overview This heatmap visualizes a classification confusion matrix, showing the distribution of true labels vs. predicted labels across six categories: blues, classical, country, jazz, metal, and unknown. The matrix uses a color gradient (light to dark blue) to represent counts, with darker shades indicating higher values. Accuracy is reported as 0.769 (76.9%). ### Components/Axes - **Y-axis (True label)**: Categories are blues, classical, country, jazz, metal, unknown (top to bottom). - **X-axis (Predicted label)**: Same categories as Y-axis (left to right). - **Legend**: Implicit via color intensity (light blue = low counts, dark blue = high counts). - **Accuracy**: 0.769 (76.9%) displayed in the bottom-right corner. ### Detailed Analysis - **Diagonal (Correct Predictions)**: - Blues: 1 (light blue) - Classical: 31 (medium blue) - Country: 22 (medium blue) - Jazz: 25 (medium blue) - Metal: 16 (medium blue) - Unknown: 128 (dark blue) - **Off-Diagonal (Misclassifications)**: - Blues → Unknown: 30 (dark blue) - Country → Unknown: 15 (medium blue) - Jazz → Unknown: 1 (light blue) - Metal → Unknown: 11 (medium blue) - Unknown → Blues: 0 - Unknown → Classical: 0 - Unknown → Country: 0 - Unknown → Jazz: 0 - Unknown → Metal: 0 - All other off-diagonal cells are 0. ### Key Observations 1. **Dominant Correct Predictions**: - The "unknown" category has the highest correct prediction count (128), suggesting it is the most frequent or easiest for the model to classify. - Classical (31) and jazz (25) follow as the next most accurately predicted categories. 2. **Significant Misclassifications**: - Blues is heavily misclassified as "unknown" (30), indicating potential overlap or ambiguity in features between these categories. - Country (15) and metal (11) also show notable misclassifications as "unknown." 3. **Low Cross-Category Errors**: - No significant misclassifications between non-unknown categories (e.g., blues → classical = 0). ### Interpretation - **Model Performance**: - The high accuracy (76.9%) is driven by the "unknown" category’s dominance (128/199 total correct predictions). - The model struggles with blues, country, and metal, which are frequently misclassified as "unknown." - **Data Imbalance**: - The "unknown" category likely dominates the dataset, skewing accuracy metrics. Without class weights, the model prioritizes the majority class. - **Feature Ambiguity**: - Blues and "unknown" share features that confuse the model, suggesting overlapping characteristics or insufficient distinguishing features. - **Actionable Insights**: - Investigate why blues and "unknown" are misclassified (e.g., feature engineering, data augmentation). - Address class imbalance via resampling or cost-sensitive learning to improve performance on minority classes.