The Decision tree model is a powerful, non-probabilistic technique, which can capture more complex non-linear patterns and feature interactions. They have been shown to perform well on many tasks, are relatively easy to understand and interpret, can handle categorical and numerical features, and do not require input data to be scaled or standardized. They are well-suited to be included in ensemble methods (for example, ensembles of decision tree models, which are called decision forests).
The decision tree model constructs a tree, where the leaves represent a class assignment to class 0 or 1, and the branches are a set of features. In the following figure, we show a simple decision tree where the binary outcome is Stay at home ...
