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
Automating Evaluation Pipelines
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- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - A book providing comprehensive guidance on building robust, scalable, and maintainable machine learning systems, including principles for MLOps, automation, and pipeline design.
- SDMetrics Official Documentation, DataCebo, 2023 (DataCebo) - Official documentation for the SDMetrics library, an open-source framework for evaluating synthetic tabular data. It details various fidelity, utility, and privacy metrics.
- Apache Airflow Documentation, The Apache Software Foundation, 2023 - The official documentation for Apache Airflow, a platform to programmatically author, schedule, and monitor workflows, highly relevant for building scalable automated evaluation pipelines.
- Synthetic Data: A Survey From Augmentation to Generation and Privacy, Yuwei Zhang, Jiannan Yang, Yunlong Zhang, Qingrui Zhang, Yang Liu, 2023 IEEE Transactions on Knowledge and Data Engineering, Vol. 36 (IEEE) DOI: 10.1109/TKDE.2023.3283626 - A recent survey providing an extensive overview of synthetic data generation techniques, evaluation methodologies, and privacy considerations, offering an academic perspective for automated evaluation.
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