Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret - KDnuggets

Here's a situation every PyCaret user is familiar with: after selecting a promising model or two from compare_models(), it's time to tune its hyperparameters to squeeze out all of the model's potential with tune_model(). By default, tune_model() uses the tried and tested RandomizedSearchCV from scikit-learn. However, not everyone knows about the various advanced options tune_model()provides. In this post, I will show you how easy it is to use other state-of-the-art algorithms with PyCaret thanks to tune-sklearn, a drop-in replacement for scikit-learn's model selection module with cutting edge hyperparameter tuning techniques. I'll also report results from a series of benchmarks, showing how tune-sklearn is able to easily improve classification model performance.