The decision tree model is a powerful, non-probabilistic technique, which can capture more complex nonlinear 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 algorithm is a top-down approach, which begins at a root node (or feature), and then selects a feature at each step that gives the best split of the dataset, as measured by the information gain of this split. The ...
