A/B testing
A/B Testing
A/B testing is a technique used to compare two versions of a model or webpage to determine which performs better. By splitting users into two groups, A and B, we can assess which version provides a superior user experience or results in better performance. A/B testing is essential for optimizing models and improving user interaction.
Consider an example where we evaluate two different models:
$$ Model_A(x) \text{ vs. } Model_B(x) $$
In A/B testing, we track performance metrics, such as accuracy or conversion rates, to decide which model performs better.
AI Terminology