Summary
We just completed the journey to building a machine learning model in scikit-learn and tracking it in Comet!
Throughout this chapter, we described some general concepts regarding machine learning as well as the main structure of the scikit-learn package. We also illustrated some important concepts, such as cross-validation, hyperparameter tuning, and the Shapley value.
In the last part of the chapter, you implemented a practical use case that showed you how to track some machine learning experiments in Comet as well as how to build a report with the results of the experiments.
In the next chapter, we will review the basic concepts related to natural language processing and how to perform it in Comet.
