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Machine Learning 101 Practice Test: Version Spaces, Find-S Algorithm And Candidate Elimination Algorithm
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A version space is a hierarchial representation of knowledge that enables you to keep track of all the useful information supplied by a sequence of learning examples without remembering any of the examples. The find-S algorithm finds the most specific hypothesis that fits all the positive examples. The algorithm considers only those positive training example. The candidate elimination algorithm incrementally builds the version space given a hypothesis space H and a set E of examples. The examples are added one by one; each example possibly shrinks the version space by removing the... Show more
Machine Learning 101 Practice Test: Version Spaces, Find-S Algorithm And Candidate Elimination Algorithm
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25 Questions

1. What is present in the version space of the Find-S algorithm in the beginning?
2. How is the version space represented?
3. S = Training data = => No (negative example). How will S be represented after encountering this training data?
4. Which is not a concept learning algorithm?
5. What is one of the drawbacks of the Find-S algorithm?
6. What is the goal of concept learning?
7. The list-then-eliminate algorithm can output more than one hypothesis.
8. S = Training data = => No (negative example). How will S be represented after encountering this training data?
9. S = . Training data = => Yes (positive example). How will S be represented after encountering this training data?
10. Candidate-Elimination algorithm can be described by ____________
11. In the list-then-eliminate algorithm, the initial version space contains _____
12. S = Training data = => Yes (positive example). How will S be represented after encountering this training data?
13. What is one of the assumptions of the Find-S algorithm?
14. A Boolean-valued function can be an example of concept learning.
15. What is one of the advantages of the Find-S algorithm?
16. What happens to the version space in the list-then-eliminate algorithm, at each step?
17. When does the hypothesis change in the Find-S algorithm, while iteration?
18. Let G be the set of maximally general hypotheses. While iterating through the dataset, when is it changed for the first time?
19. G = . Training data = => Yes (positive example). How will G be represented after encountering this training data?
20. The algorithm accommodates all the maximally specific hypotheses.
21. How does the hypothesis change gradually?
22. For a dataset with 4 attributes, which is the most general hypothesis?
23. Noise or errors in the dataset can severely affect the performance of the Find-S algorithm.
24. What is the advantage of the list-then-eliminate algorithm?
25. The algorithm is trying to find a suitable day for swimming. What is the most general hypothesis?

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