The following slides discuss the method of decision trees.
 Slide 13.1: Top 10 algorithms in data mining
Slide 13.2: Top 10 algorithms in data mining (cont.)
Slide 13.3: Decision trees (video: 7:00 minutes)
Slide 13.4: What is a decision tree?
Slide 13.5: How to use a decision tree?
Slide 13.6: How to use a decision tree? (cont.)
Slide 13.7: Measuring impurity
Slide 13.8: Entropy
Slide 13.9: Gini index
Slide 13.10: Classification error
Slide 13.11: How to generate a decision tree
Slide 13.12: How to generate a decision tree (cont.)
Slide 13.13: How to generate a decision tree (cont.)
Slide 13.14: Information gain
Slide 13.15: The first iteration for information gain
Slide 13.16: The first iteration for information gain (cont.)
Slide 13.17: The second iteration for information gain
Slide 13.18: The second iteration for information gain (cont.)
Slide 13.19: The second iteration for information gain (cont.)
Slide 13.20: The third iteration for information gain
Slide 13.21: An example of decision tree using Sklearn
Slide 13.ⓐ: Decision tree algorithm
|
|
|
|
|