Development version
You are reading the latest (dev) docs. For stable documentation, see stable.
Hyperparameter Optimization: Method Comparisons
Not every hyperparameter search method is the right tool for every problem. This section collects honest, benchmark-backed comparisons so you can make an informed choice rather than a default one.
Each page shows when sklearn-genetic-opt wins and when it doesn't. If a competing method is the better fit for your problem, we say so.
Comparisons
| Page | What it answers |
|---|---|
| Grid Search vs Random Search vs Bayesian vs Genetic Algorithms | Which method to use and why — with a fair benchmark, code for all four methods, and an honest breakdown of each method's failure modes |
| Optuna vs sklearn-genetic-opt | Head-to-head: Bayesian optimization (TPE) vs genetic algorithms — with benchmarks, code examples for the same problem, and an honest decision guide including when Optuna wins |
A Note on Honest Comparisons
Most tool documentation shows the tool at its best. We try to do better than that.
Every comparison page in this section includes:
- A scenario where sklearn-genetic-opt wins — with numbers to back it up
- A scenario where sklearn-genetic-opt loses — because it does lose, and knowing when saves you time
- Equal-budget benchmarks — comparing methods that ran the same number of evaluations, not the same wall-clock time
- Runnable code — every example uses scikit-learn's built-in datasets and runs without modification
The benchmark data on the Benchmarks page was collected with the same philosophy: the numbers that didn't go our way are published alongside the ones that did.
See Also
- When to Use sklearn-genetic-opt — the quick decision guide
- Benchmarks — full Bayesmark suite results across multiple datasets and models
- Comparing Search Methods (Example) — a copy-pasteable side-by-side with full output