This is Part 4 of Generative AI in Data Science. Do read previous parts to get a full understanding. In this blog post we will learn about benchmarking synthetic data. This post gives you a practical framework for quantifying the quality and validating the effectiveness of synthetic data for real-world model training.

Part 4: Benchmarking Synthetic Data -How Close Is “Close Enough”?

By now, we have seen how generative AI can produce surprisingly rich and useful synthetic datasets from structured tables to labeled text. But here is the question that separates experimentation from deployment:

How do we know if synthetic data is actually good enough?

You can’t just feel your way through this. You need hard metrics, comparison methods, and stress tests that tell you whether your synthetic dataset:

• Represents the real world (statistically)

• Trains usable models (functionally)

• Does not leak sensitive patterns (ethically)

This post is your guide to benchmarking synthetic data quality in a way that is grounded, measurable, and aligned with production needs.

 Benchmarking Goals

You want to evaluate synthetic data across three axes:

Let’s break down each one.

1.  Statistical Fidelity: Does It Look Real?

Your first test is distributional similarity. The synthetic data should match the real data on marginal and joint distributions.

Basic Checks

• Column-wise summary statistics

  • Mean, std, skew, kurtosis

• Correlation matrix comparison

• Data types + value ranges

Example: Pandas Profile Comparison

import pandas as pd

real_df = pd.read_csv("real_data.csv")
synth_df = pd.read_csv("synthetic_data.csv")

print(real_df.describe())
print(synth_df.describe())

Or use tools like YData Profiling:

from ydata_profiling import ProfileReport

profile_real = ProfileReport(real_df, title="Real Data")
profile_synth = ProfileReport(synth_df, title="Synthetic Data")

Visual Checks

import seaborn as sns
import matplotlib.pyplot as plt

sns.kdeplot(real_df["income"], label="Real")
sns.kdeplot(synth_df["income"], label="Synthetic")
plt.title("Income Distribution")
plt.legend()
plt.show()

You can extend this to multivariate analysis using PCA or UMAP to visualize density overlap.

2. Model Utility: Does It Actually Work?

Here is the real test: can models trained on synthetic data perform well on real data?

This is sometimes called a Train-on-Synthetic, Test-on-Real (TSTR) evaluation.

Setup

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split

# Train on synthetic
X_synth = synth_df.drop("target", axis=1)
y_synth = synth_df["target"]
clf = RandomForestClassifier()
clf.fit(X_synth, y_synth)

# Test on real
X_real = real_df.drop("target", axis=1)
y_real = real_df["target"]
y_pred = clf.predict(X_real)

print(classification_report(y_real, y_pred))

Key Metrics

• F1 Score: Does the model generalize across classes?

• AUC/ROC: Especially important for imbalanced data

• Precision/Recall: Do you miss important edge cases?

💡 Pro Tip: Run the inverse too — Train on Real, Test on Synthetic (TRTS)   to see if synthetic data reflects realistic patterns during inference.

3. Privacy & Safety: Is It Truly Synthetic?

You are not just checking for utility, you also need to ensure your synthetic data is not a leak.

Risks

• GPT can accidentally memorize training data and reproduce it verbatim.

• Overfit generators (like GANs) can echo real samples.

Testing Techniques

Distance to Nearest Neighbor (DNN)

Check if any synthetic record is too close to a real record (e.g. Euclidean or cosine distance).

from sklearn.neighbors import NearestNeighbors
import numpy as np

nbrs = NearestNeighbors(n_neighbors=1).fit(real_df.values)
distances, _ = nbrs.kneighbors(synth_df.values)

print("Avg distance:", np.mean(distances))
print("Min distance:", np.min(distances))

Low minimum distance? Red flag. You might be leaking.

Membership Inference Attacks (MIA)

Try to determine whether a model was trained on a specific datapoint. If yes, your data might be too revealing.

Note: Tools like PrivacyRaven or TensorFlow Privacy can automate MIA-style tests.

 

How Close Is “Close Enough”?

There’s no universal cutoff, but here’s a practical rule:

The answer depends on what you’re using synthetic data for:

• Bootstrapping a model? 70% realism is fine.

• Replacing a medical dataset? You’d better prove generalization and safety with rigor.

Recommended Tools

Final Thoughts

Synthetic data is not “fake” data. Done right, it’s functionally equivalent—and often better than the real thing for prototyping, privacy, and stress testing.

But you can not skip benchmarking. You need:

• Statistical checks for realism

• TSTR tests for utility

• Distance/attack metrics for safety

When synthetic data is “close enough” depends on your risk tolerance and use case. But now you’ve got the tools to know where that line is.

Up Next

In Part 5, we will explore multi-modal synthetic data generation  generating paired text + image + tabular datasets using tools like GPT-4 + DALL·E + structured prompt chaining.