Abderrahim Lyoubi-Idrissi takes us through a Bayesian approach to tune hyperparameters:

In contrast to the model parameters, which are discovered by the learning algorithm of the ML model, the so called Hyperparameter(HP) are not learned during the modeling process, but specified prior to training.

Hyperparameter tuning is the task of finding optimal hyperparameter(s) for a learning algorithm for a specific data set and at the end of the day to improve the model performance.

Abderrahim contrasts two different methods here: Grid Search and Bayesian Optimization. Definitely an interesting read if you develop data science models.

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