14. Visualization

Chapter 14 of 18 · 20 min

KEY INSIGHT

A good figure communicates a finding in seconds. A bad figure requires minutes of explanation. Invest in visualization—the effort pays back every time someone reads your work. Figures are often the first thing readers examine and the most memorable element of technical papers. Poor visualization undermines otherwise strong technical work. ### Principles of Effective Scientific Figures **Reduce cognitive load.** Every visual element should help communicate the finding. Remove decorative elements, grid lines, and chart junk. **Use appropriate encodings.** Position (x, y coordinates) is most accurately perceived. Color should be reserved for categorical distinction, not quantitative value—readers perceive color differences non-linearly. **Maximize data-ink ratio.** The Data-Ink Ratio is the proportion of ink used for actual data. Eliminate decorative borders, backgrounds, and redundant labels. ### Common Visualization Types for AI Systems **Bar charts**: Compare discrete quantities. Use when comparing 2-10 items. ```python import matplotlib.pyplot as plt import numpy as np def plot_comparison(): systems = ['Baseline', 'Ours', '+Tuning', '+Augmentation'] accuracy = [91.8, 94.2, 95.1, 95.8] errors = [0.6, 0.5, 0.4, 0.4] # Standard deviation x = np.arange(len(systems)) bars = plt.bar(x, accuracy, yerr=errors, capsize=5, color='steelblue') plt.ylabel('Accuracy (%)') plt.xticks(x, systems) plt.ylim(88, 98) # Add value labels on bars for bar, val in zip(bars, accuracy): plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3, f'{val:.1f}', ha='center', va='bottom', fontsize=10) plt.tight_layout() ``` **Line plots**: Show continuous relationships. Use when x-axis has meaningful order (dataset size, model size, iteration). **Scatter plots**: Show correlations or distributions. Essential for error analysis. ### Architecture Diagrams System architecture figures need clarity: ```mermaid graph TB A[Input Query] --> B[Retrieval Module] B --> C[Context Builder] D[Document Store] --> B C --> E[Inference Engine] E --> F[Response Formatter] F --> G[Output] style B fill:#e1f5fe style E fill:#e8f5e8 ``` Include enough detail that readers understand data flow, but not so much that the main structure is obscured. Show the happy path first; error handling and edge cases can be described in text. ### Color Palette Considerations Choose palettes that work for colorblind readers. The "jet" colormap common in Python hides information for 8% of men. Use viridis, cividis, or a custom palette designed for accessibility. If you must use color to encode information, add redundant encoding (shape, pattern) so the figure is interpretable in grayscale.

EXERCISE

Create visualizations for your research system: (1) a bar chart comparing your system to baselines on a key metric, (2) a line plot showing scaling behavior, (3) a scatter plot showing prediction quality vs. some input property (with error examples highlighted). For each, ask: can someone understand the main finding in under 5 seconds?