Decision Trees and Ensemble Methods

Decision Trees and Ensemble Methods

Machine Learning University via YouTube Direct link

DTEL3 3 2 Bootstrap

18 of 36

18 of 36

DTEL3 3 2 Bootstrap

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Decision Trees and Ensemble Methods

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  1. 1 DTEL1 - 1 Welcome to DTE class
  2. 2 DTEL1 - 2 Class Introduction
  3. 3 DTEL1 - 3 Introduction to Decision Trees
  4. 4 DTEL1 - 4 Impurity Functions
  5. 5 DTEL1 - 5 CART Algorithm
  6. 6 DTEL1 - 6 Basic Properties of Decision Trees
  7. 7 DTEL1 7 Basic Regularization of Trees
  8. 8 DTEL1 - 8 Sklearn Trees
  9. 9 DTEL1 - 9 Conclusion
  10. 10 DTEL2 2 1 Introduction
  11. 11 DTEL2 2 2 Bias variance trade off
  12. 12 DTEL2 2 3 Bias variance Decomposition
  13. 13 DTEL2 2 4 Generalizations of bias variance tradeoff
  14. 14 DTEL2 2 5 ExtraTrees Algorithm
  15. 15 DTEL2 2 6 ExtraTrees with Sklearn
  16. 16 DTEL2 2 7 Conclusion
  17. 17 DTEL3 3 1 Introduction
  18. 18 DTEL3 3 2 Bootstrap
  19. 19 DTEL3 3 3 Bagging
  20. 20 DTEL3 3 4 1 Example
  21. 21 DTEL3 3 5 Random Forest
  22. 22 DTEL3 3 4 2 Example Notebook
  23. 23 DTEL3 3 6 General Ensembling
  24. 24 DTEL4 4 1 Introduction
  25. 25 DTEL4 4 2 Proximities
  26. 26 DTEL4 4 3 Proximities Visualizations
  27. 27 DTEL4 4 4 Feature Importance's
  28. 28 DTEL4 4 5 Limitations of Tree Feature Importance
  29. 29 DTEL4 4 6 Feature Importance's in Random Forest
  30. 30 DTEL4 4 7 Summary
  31. 31 DTEL5 5 1 Introduction
  32. 32 DTEL5 5 2 Boosting
  33. 33 DTEL5 5 3 Gradient Boosting
  34. 34 DTEL5 5 4 XGBoost
  35. 35 DTEL5 5 5 LightGBM
  36. 36 DTEL5 5 6 CatBoost

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