II. Decision trees and association rules
Given by Sven Laur
Video Tutorial
Here is the set of videos to acquaint you with association rules and decision trees. Watch them in correct order.
Exercise 1
01 Rules - Support, Confidence, Coverage
02 Rules - Rule-based prediction in R
- 03 Rules - Confusion matrix, Precision, Recall
- Now start by solving exercise 1 from the exercise sheet
Exercise 2 and 3
- 04 What is a Decision Tree Algorithm
- 05 How to Compute Probability
- 06 Evaluating splits - Entropy of a Target Variable
- 07 How to Compute Entropy
- 08 Evaluating splits - Information Gain
- 09 Enumerate all Possible Attributes
- 10 Split According to an Attribute
- 11 Indexing in GRU R
- 12 Subsetting a Dataset in R
- 13 Lists in GNU R
- 14 Enumerate All Possible Splits
- 15 Enumerate All Possible Splits in R
- 16 Idea of the Recursive ID3 Algorithm
- 17 Recursive Splitter in R
- 18 When to Stop Splitting
- Now you are set to attack exercises 2 and 3
Exercise 4
- 19 Convert Decision Tree Into a Set of Rules
- This should help you with the 4th exercise
Remaining exercises
- Proceed with more open-ended exercises 5, 6 and 7
Deadline: 1st of March 16:15 EET
2. Basics of probabilistic modellingSolutions for this task can no longer be submitted.