This set of Machine Learning Multiple Choice Questions & Answers (MCQs) focuses on “Version Spaces”.

1. A Boolean-valued function can be an example of concept learning.
a) True
b) False

Explanation: Any function which can describe some concept based on datasets can be part of concept learning. For e.g. a function over pictures of animals, that is true for pictures of cats, false for others.

2. How do we learn concepts from training examples?
a) Arbitrarily
b) Decremental
c) Incrementally
d) Non-incremental

Explanation: From each training instance, a concept is enhanced. We start with a basic hypothesis. Then at each step, we go on developing the hypothesis based on the training example.

3. What is the goal of concept learning?
a) To minimize cross-validation set error
b) To maximize test set accuracy
c) To find a hypothesis that is most suitable for training instances
d) To identify all possible predictors

Explanation: Concept learning algorithms are applied to the training set. They output the hypothesis which best suits the training set, irrespective of the possible overfitting.

4. Which is not a concept learning algorithm?
a) ID3
b) Find-S
c) Candidate Elimination
d) List-Then-Eliminate

Explanation: The ID3 algorithm is one of the decision tree algorithms. All the others – find-s, candidate elimination, and list-then-eliminate are concept learning algorithms.

5. In the list-then-eliminate algorithm, the initial version space contains _____
a) most specific hypothesis
b) all hypotheses in H
c) most accurate hypothesis
d) most general hypothesis

Explanation: Initially, the version space contains all hypotheses in H, including the most specific and most general hypothesis. Gradually, it eliminates hypotheses which are not suitable for the training sets. Finally, it reaches the hypothesis which is most accurate for the training set.

6. What happens to the version space in the list-then-eliminate algorithm, at each step?
a) Remains the same
b) Increases
c) Shrinks
d) Depends on dataset

Explanation: Since the version space initially contains all the hypotheses, it gradually shrinks. At every step, it applies all the hypotheses remaining in the version space and removes each one that does not satisfy the current training example.

7. The list-then-eliminate algorithm can output more than one hypothesis.
a) True
b) False

Explanation: If the data available to the learner is insufficient, the algorithm can output all the hypotheses that still remain in the version space – they are consistent with observed data.

8. What is the advantage of the list-then-eliminate algorithm?
a) Computation is less
b) Time-effective
c) Overfitting never occurs
d) Contains all hypotheses consistent with observed data

Explanation: As the initial version space contains all hypotheses, the algorithm always outputs every hypothesis consistent with training data. A concept learning algorithm can always overfit. Since, all the hypotheses in version space are tried at every step, a lot of computation is done which takes a lot of time.

9. For a dataset with 4 attributes, which is the most general hypothesis?
a) (Sunny, Warm, Strong, Humid)
b) (Sunny, ?, ?, ?)
c) (?, ?, ?, ?)
d) (phi, phi, phi, phi)

Explanation: The most general hypothesis is the one that can accept any training example. For any attribute, the most general notation is (?). So, any hypothesis which consists of only (?) is the most general hypothesis.

10. How is a hypothesis represented in concept learning?
a) Scalar
b) Vector
c) Polynomial
d) Either scalar or vector

Explanation: The hypothesis is always expressed as a vector. If the dataset contains n independent variables, then the hypothesis is a vector with n constraints, each of which specifies one of the attributes.

Sanfoundry Global Education & Learning Series – Machine Learning.

To practice all areas of Machine Learning, here is complete set of 1000+ Multiple Choice Questions and Answers.

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