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Machine Learning: Decision Trees Questions
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Decision Trees are a type of Supervised Machine Learning (that is you explain what the input is and what the corresponding output is in the training data) where the data is continuously split according to a certain parameter. The tree can be explained by two entities, namely decision nodes and leaves.
 

Machine Learning: Decision Trees Questions
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17 Questions

1. What are Gradient Boosted Decision Trees useful for?

2. How does CART decide on decision split?

3. One of the major complaints against tree-based methods is the difficulty with _____________.

4. For decision trees ___________.

5. Rotation forest

6. L2 regularization is also commonly referred to as

7. Big decision trees tend to also fit the noise present in the underlying data and hence lead to ________________.

8. The effect of outliers is _________ on a decision tree.

9. The missing values _______affect decision trees.

10. Given this data, what is the difference between Logistic Regression and Decision Trees?

11. What does Gradient Boosted Decision Trees do?

12. Random Forest is a ______ algorithm, thus its performance is improved by combining randomly generated training sets.

13. How does the model know at what value to split the top node?

14. Attribute scaling has ______ impact on the structure of the decision trees.

15. GBDT

16. C4.5

17. Information Gain

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