Hyperparameter Search Trials Calculator

Work out hyperparameter search trials instantly with clear inputs, formula shown and shareable results.

About Hyperparameter Search Trials Calculator

Grid search cost is values raised to the power of parameters, so it explodes past three or four dimensions. Random search escapes that because the probability of missing the top p fraction of configurations in n draws is (1-p)^n: 60 random trials find a top-5-percent configuration with 95 percent probability regardless of dimensionality. Bayesian optimisation improves further by fitting a surrogate, needing roughly ten trials per dimension.

Formula

Search budgets

grid = values^parameters; random search: P(hit top p) = 1 - (1-p)^n; Bayesian: roughly 10 trials per hyperparameter

Worked example

With the default values below, Hyperparameter Search Trials Calculator returns Trials to run of 60. Change any input above and every figure here is recalculated in your browser.

Inputs used

Hyperparameters to tuneaccepts 1–30: 5
Values per hyperparameteraccepts 2–50: 4
Search strategyone of Grid search - full cross product, Random search, Bayesian optimisation: Random search
Trial budget availableminimum 1: 60

Results

Trials to run: 60
Full grid size: 1,024
Share of the grid explored: 5.8594%
Why this many: Random search finds a top-5% configuration with 95% probability in about 60 trials, independent of dimensionality

Step by step

1Values used
2Search budgets
3Trials to run= 60
4Full grid size= 1,024
5Share of the grid explored= 5.8594
6Why this many= Random search finds a top-5% configuration with 95% probability in about 60 trials, independent of dimensionality

Assumptions and limits

The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why does random search beat grid search?

Most hyperparameters barely matter. Grid search wastes trials varying unimportant dimensions at fixed values of important ones, while random search samples every dimension at a distinct value each trial.

When is grid search still appropriate?

With one or two parameters, or when you need an exhaustive, reproducible sweep for a report. Otherwise prefer random or Bayesian search.