Decision Trees

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01. Welcome To Decision Trees
02. Linearly Separable Data
03. Multiple Linear Questions
04. Constructing a Decision Tree First Split
05. Constructing a Decision Tree 2nd Split
06. Class Labels After Second Split
07. Constructing A Decision Tree/Third Split
08. Coding A Decision Tree
09. Decision Tree Accuracy
10. Decision Tree Parameters
11. Min Samples Split
12. Decision Tree Accuracy
13. Data Impurity and Entropy
14. Minimizing Impurity in Splitting
15. Formula of Entropy
16. Entropy Calculation Part 1
17. Entropy Calculation Part 2
18. Entropy Calculation Part 3
19. Entropy Calculation Part 4
20. Entropy Calculation Part 5
21. Information Gain
22. Information Gain Calculation Part 1
23. Information Gain Calculation Part 2
24. Information Gain Calculation Part 3
25. Information Gain Calculation Part 4
26. Information Gain Calculation Part 5
27. Information Gain Calculation Part 6
28. Information Gain Calculation Part 7
29. Information Gain Calculation Part 8
30. Information Gain Calculation Part 9
31. Information Gain Calculation Part 10
32. Tuning Criterion Parameter
33. Bias-Variance Dilemma
34. DT Strengths and Weaknesses
35. Decision Tree Mini-Project Video
36. Decision Tree Mini-Project
37. Your First Email DT: Accuracy
38. Speeding Up Via Feature Selection 1
39. Changing the Number of Features
40. SelectPercentile and Complexity
41. Accuracy Using 1% of Features

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41. Accuracy Using 1% of Features

Accuracy Using 1% of Features

Question:

What's the accuracy of your decision tree when you use only 1% of your available features (i.e. percentile=1)?

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