Overview#Classification and Regression Trees (CART) analysis is an umbrella term used to refer to both:
The Classification and Regression Trees algorithm is structured as a sequence of questions, the answers to which determine what the next question, if any should be. The result of these questions is a tree like structure where the ends are terminal nodes at which point there are no more questions.
The main elements of Classification and Regression Trees (and any decision tree algorithm) are:
- Rules for splitting data at a node based on the value of one variable;
- Stopping rules for deciding when a branch is terminal and can be split no more; and
- Finally, a Predictor variable for the target variable in each terminal node.
Example Classification and Regression Trees#Given a dataset with two inputs (x) of height in centimeters and weight in kilograms the output of sex as male or female, below is a crude example of a binary decision tree (completely fictitious for demonstration purposes only).
More Information#There might be more information for this subject on one of the following:
- [#1] - Decision tree learning - based on information obtained 2017-12-31
- [#2] - Classification And Regression Trees for Machine Learning - based on information obtained 2017-12-31-