Work towards a mastery of machine learning by exploring advanced decision tree algorithm concepts. Learn about the QUEST algorithm, the C5.0 algorithm, and a few advanced topics that apply to all decision trees.
- [Keith] Decision trees are without a doubt the most common predictive analytics modeling technique. Hi, I'm Keith McCormick. I've been building statistical and data mining models for 25 years and virtually every real world project that I've completed has involved decision trees at some stage of the project. I'm really excited about this class because by continuing the discussion that began in Decision Trees Part One, we can truly do a deep dive into the subject. In this course, we'll cover the QUEST algorithm and how it uses statistical tests in building its models.
We will also explore the C5.0 algorithm and its many features like winnowing and global pruning. Finally, we'll get a chance to discuss advanced topics that apply to all decision trees, like boosting, bagging, and misclassification costs. Let's begin.
- Understanding QUEST functions and applications
- C5.0 concepts and practical applications
- Understanding information gain
- Random forests
- Boosting and bagging
- Costs and priors