Hyperparameter Tuning Is Itself a Machine-Learning Problem: Grid vs Random vs Bayesian
bravo_abad · x · 2026-10-22
A long-form explainer on why hyperparameter tuning is itself a machine-learning problem: choosing learning rate, regularization strength, tree depth, or kernel width is an optimization problem that dramatically affects performance.
- Grid search enumerates every combination but only tests a few distinct values per hyperparameter, wasting budget.
- Random search usually wins at equal budget because only a few hyperparameters matter and random sampling probes more distinct values of each.
- Both share a flaw: each new configuration is chosen independently of past results.
- Bayesian optimization fixes that — after a few evaluations it builds a surrogate model and uses an acquisition function to decide where to look next.
An accompanying chart compares all three under the same 49-evaluation budget (Bayesian: 5 initial + 44 acquired), showing it concentrating samples tightly around the true optimum.
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