The following slides discuss the method of decision trees.
 Slide 12.1: Top 10 algorithms in data mining
Slide 12.2: Top 10 algorithms in data mining (cont.)
Slide 12.3: Decision trees (video: 7:00 minutes)
Slide 12.4: What is a decision tree?
Slide 12.5: How to use a decision tree?
Slide 12.6: How to use a decision tree? (cont.)
Slide 12.7: Measuring impurity
Slide 12.8: Entropy
Slide 12.9: Gini index
Slide 12.10: Classification error
Slide 12.11: How to generate a decision tree
Slide 12.12: How to generate a decision tree (cont.)
Slide 12.13: How to generate a decision tree (cont.)
Slide 12.14: Information gain
Slide 12.15: The first iteration for information gain
Slide 12.16: The first iteration for information gain (cont.)
Slide 12.17: The second iteration for information gain
Slide 12.18: The second iteration for information gain (cont.)
Slide 12.19: The second iteration for information gain (cont.)
Slide 12.20: The third iteration for information gain
Slide 12.21: A decision tree application using scikit-learn
Slide 12.a: Decision tree algorithm
|
|
|
|
|