Explaining Hyperparameter Optimization via Partial Dependence Plots

Most machine learning (ML) algorithms are highly configurable. Their hyperparameters must be chosen carefully, as their choice often impacts the model performance. Even for experts, it can be challenging to find well-performing hyperparameter configurations.