Summary
In this chapter, we discussed how several popular algorithms work at a high level, explained their important hyperparameters and how they impact performance, and provided priority lists of the hyperparameters, sorted in descending order based on their impact on performance. At this point, you should be able to design your hyperparameter tuning experiments more effectively by focusing on the most important hyperparameters. You should also understand what impact each of the important hyperparameters has on the performance of the model.
In the next chapter, we’ll summarize the hyperparameter tuning methods we’ve discussed here into a simple decision map that can help you choose which method is the most suitable for your problem. Furthermore, we will cover several study cases that show how to utilize this decision map in practice.