A Visual Guide to Bayesian Optimization

Bayesian optimization is one of the most elegant approaches to hyperparameter tuning. Instead of blindly searching a parameter space, it builds a probabilistic model of the objective function and uses it to make intelligent decisions about where to search next.

The Core Idea

At its heart, Bayesian optimization maintains a surrogate model (typically a Gaussian Process) that approximates the true objective function. It then uses an acquisition function to decide which point to evaluate next.

When to Use It

Bayesian optimization shines when:

• Your objective function is expensive to evaluate
• You have a low-dimensional parameter space
• You need a global optimum, not just a good-enough solution

Key Takeaways

The beauty of Bayesian optimization is that it balances exploration (trying new regions) with exploitation (refining known good regions) in a principled way.