🎯 Tired of Grid Search? These 3 techniques are smarter and more efficient.
Exhaustive Grid Search can take hours or days. Here are smarter alternatives that find better hyperparameters in less time:
🎲 Randomized Search — randomly samples combinations from the search space. Simple and surprisingly effective in most cases.
🧠 Bayesian Optimization — uses probabilistic models to learn from each previous trial. Each test informs the next (implemented with Optuna).
🏆 Successive Halving — starts with many configurations and few resources. Iteratively eliminates the worst, like a tournament. The fastest of all.
Comparative results with Random Forest on MNIST:
| Technique | Accuracy | Time |
|---|---|---|
| Randomized search | 96.17% | 64.6s |
| Bayesian optimization | 96.73% | 62.7s |
| Successive halving | 96.45% | 56.2s |
💡 Explanation in a nutshell#
Imagine searching for the best pizza recipe among 10,000 combinations. Grid Search tries them all. Randomized search picks some at random. Bayesian optimization learns from what it already tried: “if more cheese was good, let’s try even more cheese.” Successive Halving tests everything briefly, eliminates the bad ones, and dedicates extra time only to the most promising options.
More information at the link 👇
