Decision trees in Tableau using R
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:
Classification trees: Predicted outcome is the class the data belongs to
Regression trees: Predicted outcome is a continuous variable, for example, a real number such as the price of a commodity
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.
