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Home / Glossary / Statistical Fidelity (distribution preservation)

What is Statistical Fidelity (distribution preservation)?

Statistical Fidelity, or distribution preservation, refers to how well transformed or synthetic data retains the statistical characteristics of the original data. It measures whether important distributions, relationships, and patterns remain sufficiently preserved for analysis and execution.

Related Glossaries

  • Test data Test data refers to datasets used to evaluate the performance and accuracy of machine learning models. It is separate from training data and ensures that AI systems generalize well to new, unseen data.
  • Health data Health data refers to medical and personal health-related information collected from individuals, including electronic health records (EHRs), wearable device data, and genetic information. It is protected by privacy regulations like HIPAA and GDPR to ensure secure handling, confidentiality, and ethical…
  • Generative model A generative model is a type of machine learning algorithm that learns from data distributions to create new, realistic samples. Examples include GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), which generate images, text, and videos for applications like AI…
  • Balanced data Balanced data refers to datasets where different classes or categories are represented equally. In machine learning, balanced datasets help prevent biases in model training, ensuring fairer predictions and reducing overfitting.
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