Join Keith McCormick for an in-depth discussion in this video What you should know, part of Machine Learning: Advanced Decision Trees.
- [Narrator] This class is perfect for anyone with an interest in data science that wants to understand how decision trees work. First, I urge you to check out the first decision trees course right here in the video library. It assumes relatively little about the subject but will completely prepare you for this course. Perhaps you have a particular interest in the QUEST or C5 algorithms which we're covering in this course, so you're tempted to start with this class. Even if that's true, I would watch at least the first chapter of the previous course.
You may have noticed that I'll be doing demonstations in SPSS Modeler. If you'd like to click along, and I encourage you to, you'll pick up the basics in the first course. The focus is thoroughly on the concepts though, so you won't need to know a great deal about the software, and if you're using different software, you should be able to learn a great deal about decision trees that you can apply in any environment. How much statistics should you know? Well, the course is as much about machine learning as it is about statistics.
Some statistical terms will come up, including a discussion of F-tests, but I'm not assuming that you'll have a lot of advanced knowledge of these terms, so we won't be shy about getting into some details, but all of the prerequisite material that you might need is in machine learning essentials decision trees.
- Understanding QUEST functions and applications
- C5.0 concepts and practical applications
- Understanding information gain
- Random forests
- Boosting and bagging
- Costs and priors