Hyperparameters for machine learning algorithms. Machine learning algorithms train on data to find the best set of weights for each independent variable that affects the predicted value or class.

The choice of the optimal hyperparameters is more art than science, if we want to run it manually. Indeed, the optimal selection of the hyperparameter values depends on the problem at hand.

As the number of hyperparameters and their ranges increase, the search space expands exponentially. Consequently, Grid Search can be time-consuming, especially when applied to complex models or ...