Summary

In this chapter, you learned about techniques to improve the performance of your models and reduce their bias and variance. You learned about the different hyperparameters of widely used machine learning methods, other than deep learning, which will be covered later in the book, and Python libraries to help you in identifying the optimal hyperparameter sets. You learned about regularization as another technique to help you in training generalizable machine learning models. You also learned how to increase the quality of the data to be fed into the training process by methods such as synthetic data generation and outlier detection.

In the next chapter, you will learn about interpretability and explainability in machine learning modeling and how you can use the related techniques and Python tools to identify opportunities for improving your models.