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A/B testing

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TL;DR. A statistical method comparing two versions (A and B) of something to see which performs better.

Technical Definition

A statistical method comparing two versions (A and B) of something to see which performs better.

How it works

A/B testing is a controlled experiment method used to compare two versions of a variable, like a webpage or a model's algorithm, to determine which one performs better. Typically, one version (A) is existing, and the other (B) is modified. The test measures the impact on a key metric and uses statistical analysis to confirm if the observed difference is significant, helping data-driven decision-making.

Related Concepts

  • Evaluation — The process of measuring a model's quality or comparing different models.

Further Reading

  • Google ML Glossary