⚙️ Grid Search & Random Search

With methods like GridSearchCV or RandomizedSearchCV, the total runtime is determined by the number of parameter combinations you test. Here are two ways to speed things up:

Shrink the Parameter Grid

Instead of testing a huge range of values at once, run the search a first time to find a promising area. Then, create a new, smaller grid centered around the best results from the first run.

Example: Imagine your first search finds that the best max_depth is 5 from a range of [1, 2, ..., 10]. For your next run, you could create a much smaller grid like {'max_depth': [4, 5, 6]} to fine-tune it.

Fix Less Important Parameters

Not all hyperparameters have a major impact on your model's performance. You can identify the less critical ones and simply "hardcode" them to a reasonable default value instead of including them in your search grid. This drastically reduces the total number of combinations to test.

📈 Bayesian Optimization (like Optuna)

Optuna is more efficient, but it can still be time-consuming. The approach is similar but simpler to implement.

Reduce the Number of Trials

The easiest way to save time is to simply run fewer tests. You can do this by lowering the n_trials argument in the study.optimize() function.

Example: If a full search is taking too long, reduce the number of trials from 100 to a smaller number like 10 or 15 for a quicker, preliminary result.

From a long run...

study.optimize(objective, n_trials=100, timeout=600)

To a much faster one.

study.optimize(objective, n_trials=10)

Refine the Search Space

Just like with Grid Search, you can run a broad initial study, find the best parameters, and then adjust your search space to be a tighter range around those best values for a second, more focused study.

Example: After one run, you can print the best parameters found: print(study.best_params). If it tells you the best learning_rate was 0.05, you can change your search space from trial.suggest_loguniform('learning_rate', 0.001, 0.3) to a narrower range like trial.suggest_loguniform('learning_rate', 0.03, 0.07).