Chapter 12: Introducing Hyperparameter Tuning Decision Map
Getting too much information can sometimes lead to confusion, which can, in turn, lead back to adopting the simplest option. We learned about numerous hyperparameter tuning methods in the previous chapters. Although we have discussed the ins and outs of each method, it will be very useful for us to have a single source of truth that can be used to help us decide which method to use in which situation.
In this chapter, you’ll be introduced to the Hyperparameter Tuning Decision Map (HTDM), which summarizes all of the discussed hyperparameter tuning methods into a simple decision map based on many aspects, including the properties of the hyperparameter space, the complexity of the objective function, training data size, computational resources availability, prior knowledge availability, and the types of ML algorithms we are working with. There will be also three study cases that show how to utilize HTDM in practice.
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