What does A/B testing help determine?

A/B testing is a powerful experimental technique used primarily to compare two versions of a model or system to determine which one performs better. In the context of machine learning, it allows practitioners to evaluate the effectiveness of one version against another by measuring specific performance metrics, such as accuracy, precision, recall, or conversion rates.

When a change is made to a model—such as altering its architecture, tweaking hyperparameters, or even updating training data—A/B testing can provide empirical evidence about whether the new version leads to improvements compared to the previous one. By randomly assigning users or data instances to either the original model (the control group) or the modified version (the experimental group), analysts can assess the real-world performance of each variant, thus ensuring that any observed improvement is statistically significant.

This method helps in making informed decisions based on actual performance rather than assumptions, which is essential in the iterative process of model selection and deployment.

The other choices focus on aspects where A/B testing is not directly applicable. Identifying the best features for model selection involves techniques such as feature selection methods or algorithms rather than A/B testing. Determining the optimal training data size typically involves analyzing trade-offs between training time and model performance through various experiments, but not specifically A/B