Evaluating Synthetic Data Quality: Advanced Techniques
Chapter 1: Foundations of Synthetic Data Evaluation
Defining Data Quality Dimensions
Challenges in Evaluating Generated Data
The Fidelity-Utility-Privacy Trade-off
Taxonomy of Evaluation Metrics
Setting Up an Evaluation Environment
Chapter 2: Advanced Statistical Fidelity Assessment
Multivariate Distribution Comparisons
Hypothesis Testing for Distributional Similarity
Correlation and Covariance Structure Analysis
Information-Theoretic Measures
Propensity Score Evaluation
Hands-on practical: Implementing Multivariate Tests
Chapter 3: Evaluating Machine Learning Utility
Train-Synthetic-Test-Real (TSTR) Methodology
Train-Real-Test-Synthetic (TRTS) Methodology
Comparing Downstream Model Performance Metrics
Assessing Feature Importance Consistency
Hyperparameter Optimization Effects
Hands-on practical: Running TSTR Evaluations
Chapter 4: Privacy Assessment Techniques
Understanding Privacy Risks in Synthetic Data
Membership Inference Attacks (MIAs)
Attribute Inference Attacks
Distance-Based Privacy Metrics
Differential Privacy Considerations (if applicable)
Hands-on practical: Implementing a Basic MIA
Chapter 5: Specialized and Model-Specific Metrics
Evaluating Synthetic Images: FID, IS, Precision, Recall
Evaluating Synthetic Text: Perplexity, BLEU Scores
Evaluating Synthetic Time-Series Data
Metrics for GAN Evaluation
Metrics for VAE Evaluation
Hands-on practical: Calculating FID for Image Data
Chapter 6: Building Comprehensive Evaluation Reports
Selecting Appropriate Metrics for the Task
Automating Evaluation Pipelines
Visualizing Evaluation Results Effectively
Interpreting and Communicating Findings
Benchmarking Different Synthetic Datasets
Practice: Generating a Quality Report Snippet
Visualizing Evaluation Results Effectively
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- The Visual Display of Quantitative Information, Edward R. Tufte, 2001 (Graphics Press) - A foundational text on effective data visualization, emphasizing clarity, precision, and efficiency in graphical representation, highly relevant to the section's discussion of best practices.
- Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, Claus O. Wilke, 2019 (O'Reilly Media) - A practical guide covering various chart types and best practices for creating effective data visualizations, directly supporting the section's discussion of choosing the right visual representation.
- Membership Inference Attacks Against Machine Learning Models, Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov, 2017 2017 IEEE Symposium on Security and Privacy (SP) (IEEE) DOI: 10.1109/SP.2017.37 - The seminal paper introducing Membership Inference Attacks, detailing their methodology and evaluation using metrics like ROC curves, directly relevant to visualizing privacy risks.
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A comprehensive resource for machine learning, covering model evaluation techniques, performance metrics (like ROC/AUC), and feature importance, which are central to visualizing utility.
- A Survey of Synthetic Data Generation Methods and Evaluation Metrics, Jin-wook Cho, Kwang-il Lee, Joong-han Kim, 2020 ETRI Journal, Vol. 42 (John Wiley & Sons Inc.) DOI: 10.4218/etrij.2020-0021 - A survey paper detailing various synthetic data generation methods and a range of evaluation metrics for fidelity, utility, and privacy, providing context for the metrics being visualized.