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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

Evaluating Synthetic Data Quality: Advanced Techniques

Prerequisites: Python & ML Fundamentals

Level:

Advanced

What You'll Learn

Statistical Fidelity Assessment
Apply advanced statistical methods to compare distributions between real and synthetic datasets.
Machine Learning Utility Evaluation
Quantify the usefulness of synthetic data for training downstream machine learning models.
Privacy Risk Quantification
Implement techniques to assess the privacy leakage risks associated with synthetic datasets.
Generative Model Specific Metrics
Utilize metrics tailored for evaluating the output of specific generative models (GANs, VAEs, etc.).
Domain-Specific Evaluation
Adapt evaluation strategies for specialized data types like time-series or sequential data.
Implementation of Evaluation Pipelines
Build automated pipelines for comprehensive synthetic data quality reporting.

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