Adaptive Experimentation for Model Optimization

As established in Chapter 16 , traditional A/B/n tests are ill-suited for hyperparameter tuning: grid and random search waste experiment traffic on unpromising configurations, while live evaluations are expensive and time-consuming. This chapter introduces adaptive experimentation—a family of intelligent optimization techniques that learn from each experiment iteration to make smarter decisions about which configurations to test next. We will focus specifically on Bayesian Optimization, a powerful framework that uses probabilistic models to navigate complex parameter spaces with far fewer evaluations than classical methods.