## Line Graph: Accuracy vs. Epochs for Different Methods ### Overview The image is a line graph comparing the accuracy (%) of three methods—**de Bruijn**, **Random Vars**, and **Traditional**—over 50 epochs. All three lines show an upward trend in accuracy, with convergence toward higher values as epochs increase. The graph includes a legend, axis labels, and gridlines for reference. --- ### Components/Axes - **X-axis (Epochs)**: Labeled "Epochs," ranging from 0 to 50 in increments of 10. - **Y-axis (Accuracy %)**: Labeled "Accuracy (%)", ranging from 0 to 100 in increments of 5. - **Legend**: Located in the **bottom-right corner**, with color-coded labels: - **Green**: de Bruijn - **Blue**: Random Vars - **Purple**: Traditional - **Gridlines**: Horizontal and vertical gridlines divide the plot into 1% and 1-epoch increments. --- ### Detailed Analysis 1. **de Bruijn (Green Line)**: - Starts at ~10% accuracy at epoch 0. - Sharp rise to ~75% by epoch 5, followed by gradual stabilization. - Fluctuates slightly between 80–90% after epoch 10, ending near 88% at epoch 50. 2. **Random Vars (Blue Line)**: - Starts at ~15% accuracy at epoch 0. - Rapid increase to ~80% by epoch 10, with notable volatility (e.g., dips to ~75% at epoch 20). - Stabilizes between 85–90% after epoch 25, ending near 89% at epoch 50. 3. **Traditional (Purple Line)**: - Starts at ~5% accuracy at epoch 0. - Slow initial rise to ~70% by epoch 10, followed by accelerated growth. - Peaks at ~92% by epoch 35, then fluctuates between 88–93% until epoch 50. --- ### Key Observations - **Initial Disparity**: At epoch 0, Traditional has the lowest accuracy (~5%), while Random Vars starts highest (~15%). - **Convergence**: By epoch 30, all lines cluster between 85–90%, indicating similar performance in later stages. - **Volatility**: Random Vars exhibits the most fluctuation (e.g., sharp dips at epochs 20 and 35), while Traditional and de Bruijn show smoother trends. - **Final Performance**: Traditional achieves the highest accuracy (~93%) by epoch 50, followed by de Bruijn (~88%) and Random Vars (~89%). --- ### Interpretation The data suggests that all three methods improve accuracy with increased training epochs, but their learning curves differ: - **Traditional** starts slow but outperforms others in the long run, possibly due to a more stable optimization process. - **de Bruijn** achieves rapid early gains but plateaus earlier, suggesting diminishing returns after epoch 10. - **Random Vars** shows high variability, potentially due to stochastic elements in its design, leading to less consistent performance. The convergence of lines after epoch 10 implies that all methods reach a similar "ceiling" in accuracy, though Traditional maintains a slight edge. The fluctuations in Random Vars highlight the trade-off between exploration (via randomness) and stability in model training.