A crucial step in creating successful and efficient machine learning models is hyperparameter tweaking. Hyperparameters are the settings made prior to the learning process starting, like the kernel used in a support vector machine, the number of trees in a forest, or the learning rate. Hyperparameters are externally defined and have a major impact