## Line Chart: Accuracy vs. Varying Maximum Depth ### Overview The image is a line chart comparing the accuracy of two models, PoG and PoG-E, across varying maximum depths (Dmax) from 1 to 4. The y-axis represents accuracy in percentage, ranging from 50% to 85%. The x-axis represents the varying maximum depth (Dmax). ### Components/Axes * **X-axis:** Varying maximum depth (Dmax), with values 1, 2, 3, and 4. * **Y-axis:** Accuracy (%), ranging from 50 to 85, with gridlines at intervals of 5. * **Legend:** Located at the bottom of the chart. * Blue line with downward-pointing triangle markers: PoG * Black line with diamond markers: PoG-E ### Detailed Analysis * **PoG (Blue Line):** * Trend: The accuracy of PoG increases from Dmax = 1 to Dmax = 3, then plateaus. * Data Points: * Dmax = 1: Accuracy ≈ 62.5% * Dmax = 2: Accuracy ≈ 73.5% * Dmax = 3: Accuracy ≈ 80.5% * Dmax = 4: Accuracy ≈ 80.5% * **PoG-E (Black Line):** * Trend: The accuracy of PoG-E increases from Dmax = 1 to Dmax = 3, then decreases. * Data Points: * Dmax = 1: Accuracy ≈ 55.5% * Dmax = 2: Accuracy ≈ 69% * Dmax = 3: Accuracy ≈ 78.5% * Dmax = 4: Accuracy ≈ 70% ### Key Observations * Both models show an increase in accuracy as the maximum depth increases from 1 to 3. * PoG-E's accuracy decreases when the maximum depth is increased from 3 to 4, while PoG's accuracy remains relatively constant. * PoG consistently outperforms PoG-E at all maximum depths. ### Interpretation The chart suggests that increasing the maximum depth of the models initially improves their accuracy. However, for PoG-E, there is a point where increasing the depth further leads to a decrease in accuracy, possibly due to overfitting. PoG appears to be more stable at higher depths, maintaining its accuracy. The data indicates that PoG is the superior model across the tested range of maximum depths. The optimal depth for PoG-E appears to be around 3, while PoG performs well at both 3 and 4.

## Line Chart: Accuracy vs. Varying Maximum Depth ### Overview This image displays a line chart illustrating the accuracy (%) of two different methods, "PoG" and "PoG-E", as a function of varying maximum depth ($D_{max}$). The chart shows how the accuracy changes for each method across different depth values. ### Components/Axes * **Y-axis Title**: "Accuracy (%)" * **Scale**: Ranges from 50 to 85, with major grid lines at intervals of 5 (50, 55, 60, 65, 70, 75, 80, 85). * **X-axis Title**: "Varying maximum depth ($D_{max}$)" * **Scale**: Ranges from 1 to 4, with major grid lines at integer values (1, 2, 3, 4). * **Legend**: Located in the bottom-center of the chart. * "PoG": Represented by blue upward-pointing triangles. * "PoG-E": Represented by black diamonds. ### Detailed Analysis **Data Series: PoG (Blue Upward-Triangles)** * **Trend**: The "PoG" line shows an upward trend from $D_{max}=1$ to $D_{max}=3$, and then plateaus from $D_{max}=3$ to $D_{max}=4$. * **Data Points**: * At $D_{max}=1$: Accuracy is approximately 63%. * At $D_{max}=2$: Accuracy is approximately 74%. * At $D_{max}=3$: Accuracy is approximately 81%. * At $D_{max}=4$: Accuracy is approximately 81%. **Data Series: PoG-E (Black Diamonds)** * **Trend**: The "PoG-E" line shows an upward trend from $D_{max}=1$ to $D_{max}=3$, and then a downward trend from $D_{max}=3$ to $D_{max}=4$. * **Data Points**: * At $D_{max}=1$: Accuracy is approximately 55%. * At $D_{max}=2$: Accuracy is approximately 69%. * At $D_{max}=3$: Accuracy is approximately 78%. * At $D_{max}=4$: Accuracy is approximately 70%. ### Key Observations * Both "PoG" and "PoG-E" methods show an increase in accuracy as the maximum depth increases from 1 to 3. * The "PoG" method achieves a higher accuracy than "PoG-E" at $D_{max}=1$ and $D_{max}=2$. * At $D_{max}=3$, "PoG" reaches its peak accuracy of approximately 81%, while "PoG-E" reaches approximately 78%. * The "PoG" method maintains its peak accuracy from $D_{max}=3$ to $D_{max}=4$. * The "PoG-E" method experiences a significant drop in accuracy from $D_{max}=3$ to $D_{max}=4$, falling to approximately 70%. ### Interpretation The data suggests that increasing the maximum depth ($D_{max}$) generally improves the accuracy of both "PoG" and "PoG-E" methods, up to a certain point. The "PoG" method appears to be more robust to increasing depth, reaching a plateau and maintaining its performance. In contrast, the "PoG-E" method shows signs of overfitting or diminishing returns beyond a depth of 3, as its accuracy decreases significantly at $D_{max}=4$. This indicates that for the "PoG-E" method, a maximum depth of 3 might be optimal, while for "PoG", a depth of 3 or 4 yields the best results. The "PoG" method consistently outperforms "PoG-E" at lower depths and maintains a higher accuracy at the optimal depth.

\n ## Line Chart: Accuracy vs. Maximum Depth ### Overview This line chart depicts the relationship between accuracy (in percentage) and varying maximum depth (D_max) for two different methods: PoG and PoG-E. The chart shows how the accuracy of each method changes as the maximum depth increases from 1 to 4. ### Components/Axes * **X-axis:** "Varying maximum depth (D_max)" with markers at 1, 2, 3, and 4. * **Y-axis:** "Accuracy (%)" with a scale ranging from 50% to 85%. * **Legend:** Located at the top-center of the chart. * **PoG:** Represented by a blue line with downward-pointing triangle markers. * **PoG-E:** Represented by a black line with diamond markers. * **Gridlines:** Horizontal and vertical gridlines are present to aid in reading values. ### Detailed Analysis **PoG (Blue Line):** The PoG line starts at approximately 63% accuracy at D_max = 1. It then slopes upward, reaching approximately 73% at D_max = 2, approximately 80% at D_max = 3, and plateaus at approximately 80% for D_max = 4. * D_max = 1: Accuracy ≈ 63% * D_max = 2: Accuracy ≈ 73% * D_max = 3: Accuracy ≈ 80% * D_max = 4: Accuracy ≈ 80% **PoG-E (Black Line):** The PoG-E line begins at approximately 55% accuracy at D_max = 1. It increases sharply to approximately 70% at D_max = 2, continues to rise to approximately 80% at D_max = 3, and then decreases to approximately 70% at D_max = 4. * D_max = 1: Accuracy ≈ 55% * D_max = 2: Accuracy ≈ 70% * D_max = 3: Accuracy ≈ 80% * D_max = 4: Accuracy ≈ 70% ### Key Observations * PoG-E starts with lower accuracy than PoG at D_max = 1. * Both methods show increasing accuracy as D_max increases from 1 to 3. * PoG reaches a plateau in accuracy at D_max = 3, while PoG-E's accuracy decreases at D_max = 4. * At D_max = 3, both methods achieve the same accuracy of approximately 80%. ### Interpretation The data suggests that increasing the maximum depth (D_max) generally improves the accuracy of both PoG and PoG-E methods, up to a certain point. PoG appears to be less sensitive to further increases in depth beyond D_max = 3, maintaining a consistent accuracy. PoG-E, however, experiences a decline in accuracy at D_max = 4, indicating that exceeding a certain depth may introduce errors or inefficiencies for this method. The plateauing of PoG suggests that the benefits of increasing depth diminish after a certain point, potentially due to limitations in the algorithm or the data itself. The decrease in PoG-E's accuracy at D_max = 4 could be due to overfitting or increased computational complexity leading to errors. The initial lower accuracy of PoG-E compared to PoG indicates that PoG-E may require a larger depth to achieve comparable performance. The convergence of the two methods at D_max = 3 suggests that both methods can achieve similar levels of accuracy with appropriate parameter tuning.

## Line Chart: Accuracy vs. Maximum Depth for PoG and PoG-E Methods ### Overview This is a line chart comparing the performance, measured in accuracy percentage, of two methods—PoG and PoG-E—as a function of a parameter called "Varying maximum depth (D_max)". The chart plots accuracy on the vertical axis against discrete depth values on the horizontal axis. ### Components/Axes * **Chart Type:** Line chart with markers. * **X-Axis (Horizontal):** * **Label:** "Varying maximum depth (D_max)" * **Scale:** Discrete integer values: 1, 2, 3, 4. * **Y-Axis (Vertical):** * **Label:** "Accuracy (%)" * **Scale:** Linear scale from 50 to 85, with major gridlines every 5 units (50, 55, 60, ..., 85). * **Legend:** * **Position:** Centered at the bottom of the chart area. * **Series 1:** "PoG" - Represented by a solid blue line with downward-pointing triangle markers (▼). * **Series 2:** "PoG-E" - Represented by a solid black line with diamond markers (◆). * **Grid:** A light gray grid is present, with both horizontal and vertical lines aligned with the axis ticks. ### Detailed Analysis **Data Series: PoG (Blue line, ▼ markers)** * **Trend:** The line shows a consistent upward trend that plateaus at the highest depth values. * **Data Points (Approximate):** * At D_max = 1: Accuracy ≈ 62.5% * At D_max = 2: Accuracy ≈ 73.5% * At D_max = 3: Accuracy ≈ 80.5% * At D_max = 4: Accuracy ≈ 80.5% **Data Series: PoG-E (Black line, ◆ markers)** * **Trend:** The line shows an initial upward trend, peaks at D_max=3, and then declines. * **Data Points (Approximate):** * At D_max = 1: Accuracy ≈ 55.5% * At D_max = 2: Accuracy ≈ 69.0% * At D_max = 3: Accuracy ≈ 78.5% * At D_max = 4: Accuracy ≈ 70.0% ### Key Observations 1. **Performance Gap:** The PoG method consistently achieves higher accuracy than the PoG-E method at every measured depth value. 2. **Peak Performance:** Both methods reach their peak accuracy at D_max = 3. PoG's peak is ~80.5%, while PoG-E's peak is ~78.5%. 3. **Divergent Behavior at High Depth:** At the maximum depth of 4, the two methods diverge significantly. PoG maintains its peak accuracy (plateaus), while PoG-E's accuracy drops sharply by approximately 8.5 percentage points from its peak. 4. **Rate of Improvement:** The most significant gains in accuracy for both methods occur when increasing D_max from 1 to 2. ### Interpretation The chart demonstrates the relationship between model complexity (controlled by maximum depth, D_max) and predictive accuracy for two related algorithms. The data suggests: * **Benefit of Increased Depth:** For both methods, increasing the maximum depth from 1 to 3 leads to substantial improvements in accuracy, indicating that allowing the model to consider deeper hierarchical structures or longer sequences is beneficial up to a point. * **Robustness vs. Overfitting:** The PoG method appears more robust to increases in model complexity. Its performance plateaus at D_max=3 and 4, suggesting it has reached its capacity or that additional depth provides no further benefit. In contrast, the PoG-E method's performance degrades at D_max=4, which is a classic sign of overfitting—the model may be becoming too complex and fitting noise in the training data, harming its generalization performance. * **Method Superiority:** Based on this evaluation, PoG is the superior method across the tested range of depths, offering both higher peak accuracy and more stable performance as complexity increases. The "E" variant (PoG-E) may incorporate a modification that introduces instability or sensitivity at higher depths. **Language Note:** All text in the image is in English. No other languages are present.

## Line Graph: Accuracy vs. Varying Maximum Depth (D_max) ### Overview The image depicts a line graph comparing the accuracy of two methods, **PoG** and **PoG-E**, across varying maximum depths (D_max = 1 to 4). The y-axis represents accuracy in percentage (%), while the x-axis represents discrete depth values. The graph highlights trends in performance as depth increases. --- ### Components/Axes - **X-axis (Horizontal)**: Labeled "Varying maximum depth (D_max)" with discrete values: 1, 2, 3, 4. - **Y-axis (Vertical)**: Labeled "Accuracy (%)" with a scale from 50% to 85%. - **Legend**: Located in the **bottom-right corner**, with: - **Blue triangles** representing **PoG**. - **Black diamonds** representing **PoG-E**. --- ### Detailed Analysis #### PoG (Blue Triangles) - **D_max = 1**: ~62.5% accuracy. - **D_max = 2**: ~73.5% accuracy. - **D_max = 3**: ~80.5% accuracy. - **D_max = 4**: ~80.3% accuracy. - **Trend**: Steady increase from D_max = 1 to 3, followed by a slight plateau/decline at D_max = 4. #### PoG-E (Black Diamonds) - **D_max = 1**: ~55.5% accuracy. - **D_max = 2**: ~69% accuracy. - **D_max = 3**: ~78% accuracy. - **D_max = 4**: ~70% accuracy. - **Trend**: Initial rise from D_max = 1 to 3, followed by a sharp decline at D_max = 4. --- ### Key Observations 1. **PoG outperforms PoG-E** across all depths, with a maximum accuracy of ~80.5% (D_max = 3) compared to PoG-E's ~78%. 2. **PoG-E shows a significant drop** in accuracy at D_max = 4 (~70%), suggesting potential overfitting or instability at higher depths. 3. **PoG maintains higher consistency**, with only a marginal decrease (~0.2%) between D_max = 3 and 4. 4. Both methods exhibit **non-linear behavior**, with PoG-E's performance being more volatile. --- ### Interpretation - **PoG's superiority** suggests it is more robust to depth variations, possibly due to better generalization or simpler architecture. - **PoG-E's decline at D_max = 4** implies that increasing depth beyond 3 introduces noise or complexity that harms performance, a common issue in deep learning models. - The **plateau in PoG** at D_max = 3–4 indicates diminishing returns, suggesting an optimal depth range of 3 for maximum accuracy. - The graph underscores the trade-off between model complexity (depth) and performance, with PoG achieving higher accuracy with relatively stable behavior.