Summary

This was a very practical and hands-on chapter in which we developed some standard code to train and evaluate multiple machine learning models. Then, we reviewed a few key machine learning models like ridge regression, lasso regression, decision trees, Random Forest, and gradient-boosted trees and how they work behind the hood. To complete and reinforce what we learned, we applied the machine learning models we learned about to the London Smart Meters dataset and saw how well they did. This chapter sets you up to tackle the coming chapters, where we will use the standardized code and these models to go deeper into forecasting with machine learning.

In the next chapter, we will start combining different forecasts into a single forecast and explore concepts such as combinatorial optimization and stacking to achieve state-of-the-art results.