基于KNIME的机器学习和人工智能基础决策树

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上一讲： 12. When to turn off pruning

下一讲： 2. What is the Gini coefficient

视频列表

0 - Introduction
1. The basics of decision trees
2. What you should know
3. How to use the practice files
1 - Introducing Decision Trees
1. What is a decision tree
2. The pros and cons of decision trees
3. Introducing KNIME
4. A quick review of machine learning basics with examples
5. An overview of decision tree algorithms
2 - Introducing the C5.0 Algorithm
1. Ross Quinlan, ID3, C4.5, and C5.0
2. Understanding the entropy calculation
3. How C4.5 handles missing data
4. The Give Me Some Credit data set
5. Working with the prebuilt example
6. KNIME settings for C4.5
7. How C4.5 handles nominal variables
8. How C4.5 handles continuous variables
9. Equal size sampling
10. A quick look at the complete C4.5 tree
11. Evaluating the accuracy of your C4.5 tree
12. When to turn off pruning
3 - Introducing Classification Trees
1. Introducing Leo Breiman and CART
2. What is the Gini coefficient
3. How CART handles missing data using surrogates
4. Changing the settings in KNIME
5. How CART handles nominal variables
6. A quick look at the complete CART tree
7. Evaluating the accuracy of your CART tree
4 - Introducing Regression Trees
1. MPG data set
2. The regression tree prebuilt example
3. The math behind regression trees
4. How RT handles nominal variables
5. Ordinal variable handling
6. Closer look at a full regression tree
7. KNIME's missing data options for regression trees
8. Line plot
9. Accuracy
5 - Conclusion
1. Next steps