Decision Trees and Random Forests

As stressed at the beginning of this chapter, our main aim here is to show that applying machine learning methods is made fairly easy with application packages like sklearn, although one still needs to know how to use techniques like hyperparameter tuning and balancing data to make effective use of them.

In the next three lessons, we want to explain some of the ideas behind the specific models implemented by the random forest classifier $\text{(RFC)}$ and the support vector machine $\text{(SVM)}$. This is followed in the next chapter by discussions of neural networks. The next two lessons are optional, in the sense that following the theory behind them really requires knowledge of additional mathematical concepts that are beyond our brief introductory treatment in this course. Instead, the main focus here is to give a glimpse of the deep thoughts behind those algorithms and to encourage the interested reader to engage with further studies.