Unit 06 | Rules: Classification with Decision Trees and Rules |
| 9.5 hrs |
Upon completion of this module, you will be able to:
- explain the Decision Trees for rule-based classification
- automatically construct decision points with C5.0, 1R, and RIPPER
- apply Decision Trees to classification problems
- implement Decision Trees and Rules in R through code and through packages
- appreciate Divide-and-Conquer algorithms
- list the top five machine learning algorithms in common use
Divide and Conquer
|
Required Work
Additional ResourcesAdvanced ReadingsSlide Deck & Data Sets |
The C5.0 Decision Tree Algorithm
|
Required Work
Additional Resources
Advanced Readings |
Rule-Based Learners: 1R and RIPPER
|
Required Work
Additional Resources
|
Optional Guest Lecture: Decision Trees
|
Optional Work
Additional Resources
Data Sets
|
Required Guest Lecture: Top Five Algorithms in Data Mining
|
Required WorkThis (1:13hr long) guest lecture will give you an overview of the most common algorithms that are used in Data Science. You will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. This introduces algorithms that we have not yet seen but will soon see, starting next week.
Required Reading |