As we discussed in the chapter introduction, generating synthetic data is only the first step. We need to verify if the generated data is actually useful and resembles the original data it's meant to mimic. One of the most intuitive ways to start this evaluation is through visual inspection. This involves using graphs and plots to look at the synthetic data, often comparing it side-by-side with the real data. While it might seem basic, visual checks are often the quickest way to spot significant discrepancies or confirm that the generation process is heading in the right direction.
Visual inspection relies on our pattern-recognition abilities. By plotting the data in different ways, we can get a feel for its structure, distributions, and relationships between variables. This approach is particularly helpful for understanding overall shapes and trends, even if it doesn't provide precise numerical scores.
For numerical features (like age, price, or sensor readings), histograms and density plots are excellent tools. They show how frequently different ranges of values occur in your dataset.
To evaluate your synthetic data, you can create histograms or density plots for a specific feature from both your real and synthetic datasets and plot them together.
Here's an example comparing the distribution of a hypothetical 'Age' feature:
Comparison of age distribution using overlaid histograms. We are looking for similarities in shape, central tendency, and spread between the real (blue) and synthetic (orange) data.
Significant differences in shape or range might indicate that the synthetic generation process isn't capturing this feature's characteristics well.
When you have multiple numerical features, scatter plots help visualize the relationship between pairs of them. For instance, you might plot 'Height' vs. 'Weight' or 'Temperature' vs. 'Pressure'.
By creating scatter plots for the same pair of features from both the real and synthetic datasets, you can compare the patterns.
Consider this example comparing 'Feature A' and 'Feature B':
Scatter plot comparing the relationship between two features in the real (blue circles) and synthetic (orange crosses) datasets. Look for similar trends and point distributions.
If the real data shows a clear diagonal trend and the synthetic data shows a random cloud of points, the generation method failed to capture the relationship between these features.
For categorical features (like 'Product Category', 'City', or 'User Type'), bar charts are useful. They show the frequency or proportion of each category.
You can create bar charts for a categorical feature from both datasets to compare the counts or percentages of each category.
Example comparing 'Product Category' frequencies:
Grouped bar chart comparing the counts of different product categories in the real (blue) and synthetic (orange) datasets. We check if the proportions look reasonably similar.
For synthetic image data, visual inspection is often the primary method, especially at a basic level.
While simple, seeing the images directly gives immediate feedback on the generation process's output quality.
Visual inspection is a powerful first line of defense in evaluation:
Strengths:
Limitations:
Visual inspection is an indispensable starting point for evaluating synthetic data. It provides immediate, qualitative feedback. However, because of its limitations, it should always be complemented by the more quantitative statistical comparisons and utility-based evaluations that we will discuss next.
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