Bayesian Optimization is a popular searching algorithm for hyper-parameters in the machine learning area.
There are also two popular Python libraries for this algorithm: Hyperopt and Optuna. So I have done some experiments on these two libraries. The trial is using LightGBM to classify tabular data, and the hyper-parameters and their ranges are:

  • n_estimators (10~100000)
  • learning_rate (0.0001~1.0)
  • num_leaves (2, 2000)
  • max_depth (2, 200)
  • min_child_samples (1, 10000)
  • reg_alpha (0.001, 10.0)
  • reg_lambda (0.001, 10.0)

I run 10 trials each time for 10 times, by using different algorithms in Hyperopt and Optuna. The result for Hyperopt is:


And the result for Optuna is:


Seems that for my tabular data, TPE and Annealing works best, and Hyperopt works better than Optuna