Many times you hear about decision trees used into operation research. In general, they are used to develop strategies to reach a goal. However, Data Science makes use of them too. When thinking about decision trees and their building blocks it is important to consider many things:
- Tree elements
- Tree rules
- Tree flowchart
It is important to understand the pros and cons of decision trees and how they should be used. Many users of decision trees do not understand the full power of association rule induction. Take a minute to watch the short 7 minute video.
Last episode, we treated our Decision Tree as a blackbox. In this episode, we’ll build one on a real dataset, add code to visualize it, and practice reading it – so you can see how it works under the hood. And hey — I may have gone a little fast through some parts.
If you want to dig in a bit more take a look at the following references:
- Quinlan, J. R. (1987). Simplifying decision trees. International journal of man-machine studies, 27(3), 221-234.
- Karimi, K., & Hamilton, H. J. (2010). Generation and Interpretation of Temporal Decision Rules. arXiv preprint arXiv:1004.3334.
- Quilan, J. R. (1983). Learning efficient classification procedures and their application to chess end games. Machine Learning: An Artificial Intelligence Approach, 1.