Summary
In this chapter, we introduced machine learning as a method of creating software by learning to perform a task from a corpus of data instead of relying on programming the instructions by hand. We introduced the core concepts of machine learning with a focus on supervised learning and illustrated their applications through examples with scikit-learn.
We also introduced decision trees as a machine learning algorithm and discussed their strengths and weaknesses, as well as how to control overfitting using hyperparameters. We concluded this chapter with examples of how to solve classification and regression problems using decision trees in scikit-learn.
This chapter has given us a foundational understanding of machine learning, enabling us to dive deeper into the data science process and the LightGBM library.
The next chapter will focus on ensemble learning in decision trees, a technique where the predictions of multiple decision trees are combined to improve the overall performance. Boosting, particularly gradient boosting, will be covered in detail.
