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Decision Tree Learning: A Tool to Predict C-Section Risk

Learn about Decision Trees, ID3 Algorithm, and post-pruning in machine learning with insights into avoiding overfitting and making informed predictions. Uncover the process of top-down induction, entropy, and information gain to create effective decision trees. Explore Occam’s Razor principle and Reduced-Error Pruning to refine your models for accurate risk assessment. Discover how to handle unknown attribute values and optimize attribute selection to enhance decision tree learning.

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Decision Tree Learning: A Tool to Predict C-Section Risk

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  1. IES 511Machine Learning Dr. Türker İnce(Lecture notes by Prof. T. M. Mitchell, Machine Learning course at CMU) Decision Trees ID3 learning algorithm Post-pruning trees

  2. Sample Decision Tree

  3. Decision Tree Learning

  4. Decision Tree Learning

  5. A Tree to Predict C-Section Risk

  6. When to Consider Decision Trees

  7. Top-Down Induction of Decision Trees

  8. Sample Entropy

  9. Information Gain

  10. Training Examples

  11. Selecting the Next Attribute

  12. Selecting the Next Attribute

  13. Hypothesis Space Search by ID3

  14. Hypothesis Space Search by ID3

  15. Why Prefer Short Hypothesis? (Occam’s Razor)

  16. Overfitting in Decision Trees

  17. Overfitting

  18. Overfitting in Decision Tree Learning

  19. Avoiding Overfitting

  20. Reduced-Error Pruning

  21. Effect of Reduced-Error Pruning

  22. Rule Post-Pruning

  23. Unknown Attribute Values

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