Hyperparameter optimization

Hyperparameter optimization is the process of finding the best values for settings that control how a machine learning algorithm works. It helps us find the best way to solve a problem using an algorithm.

For example, let's say you are making a machine learning system to help you find the best way to make cookies. You need to set a bunch of settings to help your machine decide how it should go about solving the problem. You might need to decide how far apart to put the ingredients, how much of each ingredient to use, or how much time to cook them for.

Hyperparameter optimization is the process of trying out different combinations of settings to see which works the best. The algorithm will go through lots of different combinations and pick the one that works the best. We can use this process to find the best way to make our cookies!