I often hear people say, "I use Hyperopt for hyperparameter tuning," or "Optuna produces the best results."
It almost sounds like these libraries work some kind of magic, but the reality is that they implement well-researched methods. Yes, they’re effective, but they’re not magic—they’re based on math.
Both Optuna and Hyperopt utilize common techniques, including:
- Bayesian Optimization using Tree Parzen Estimators as surrogates
- Randomized Search
However, they also have distinct differences:
- Optuna makes it easier to set up searches
- Optuna has better documentation
- Optuna supports additional algorithms, including multifidelity optimization, Bayesian optimization with Gaussian processes, grid search, and genetic algorithms
🧐That said, neither Optuna nor Hyperopt currently supports Bayesian optimization with random forests as a surrogate, which some studies suggest is the best option for hyperparameter tuning.
Additionally, scikit-learn provides randomized search and multifidelity models as well. So why would you use something else when training scikit-learn models?
💡In short, understanding how these libraries work empowers you to choose the one that best fits your needs rather than adapting your needs to fit a library.
Knowledge is power! 💪
🎓Check out my course, Hyperparameter Optimization for Machine Learning, for comprehensive learning.
I hope this information was useful!
Wishing you a successful week ahead - see you next Monday! 👋🏻
Sole
