When the data has a lot of features that interact in complicated non-linear ways, it is hard to find a global regression model, that is, a single predictive formula that holds over the entire dataset. An alternative approach is to partition the space into smaller regions, then into sub-partitions (recursive partitioning) until each chunk can be explained with a simple model.
There are two main types of decision trees:
There are many ensemble machine learning methods that take advantage of decision trees. Perhaps the best known is the Random Forest classifier that constructs multiple decision trees and outputs the class that corresponds to the mode of the classes output by individual trees.