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
Assessing Feature Importance Consistency
Was this section helpful?
- A Unified Approach to Interpreting Model Predictions, Scott M. Lundberg and Su-In Lee, 2017 Advances in Neural Information Processing Systems 30, Vol. 30 (Curran Associates, Inc.) DOI: 10.5555/3295222.3295325 - Foundational paper introducing SHAP (SHapley Additive exPlanations) values for consistent and locally accurate feature attributions.
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Christoph Molnar, 2020 (Lulu.com (self-published online book)) - A comprehensive online book covering various feature importance methods (including permutation importance) and model interpretability techniques.
- Permutation importance, scikit-learn developers, 2023 - Official documentation for scikit-learn's
permutation_importancefunction, including explanation and usage examples.
© 2025 ApX Machine LearningEngineered with