Summary
In this chapter, we learned how to build decision trees for regression and classification tasks. We saw that although the idea is simple, there are several decisions that we have to make in order to construct our tree model, such as what splitting criterion to use, as well as when and how to prune our final tree.
In each case, we considered a number of viable options and it turns out that there are several algorithms that are used to build decision tree models. Some of the best qualities of decision trees are the fact that they are typically easy to implement and very easy to interpret, while making no assumptions about the underlying model of the data. Decision trees have native options for performing feature selection and handling missing data, and are very capable of handling a wide range of feature types.
Having said that, we saw that from a computational perspective, finding a split for categorical variables is quite expensive due to the exponential growth of the number of possible...
