What does A/B testing in machine learning refer to?

A/B testing in machine learning refers to the process of comparing two versions of a model or algorithm to evaluate which one performs better in terms of defined metrics such as accuracy, precision, or recall. This method allows practitioners to discern the effectiveness of changes or variations made to the model.

In a typical A/B test, participants or data points are randomly assigned to one of two groups: Group A, which uses the original model, and Group B, which uses the modified model. By analyzing the output and performance of both groups, data scientists can determine which model yields superior results. This approach is crucial for optimizing models based on empirical evidence rather than intuition or assumptions, leading to improved model performance and better decision-making in various applications.

The other options do not align with the traditional definition of A/B testing in the context of machine learning. Testing user interface designs, analyzing data storage efficiency, and evaluating cloud service costs are important aspects of software development and cloud management but do not encompass the comparative performance evaluation inherent in A/B testing for machine learning models.