Model optimization

As you know, understanding evaluation metrics is very important in order to measure your model’s performance and document your work. In the same way, when you want to optimize your current models, evaluating metrics also plays a very important role in defining the baseline performance that you want to challenge.

The process of model optimization consists of finding the best configuration (also known as hyperparameters) of the machine learning algorithm for a particular data distribution. You do not want to find hyperparameters that overfit the training data, in the same way that you do not want to find hyperparameters that underfit the training data.

You learned about overfitting and underfitting in Chapter 1, Machine Learning Fundamentals. In the same chapter, you also learned how to avoid these two types of modeling issues.

In this section, you will learn about some techniques that you can use to find the best configuration for a particular algorithm...