Question

In the following table, the Task column lists a few tasks related to machine learning. The Algorithm column lists a few algorithms.
Each entry "t" from the Task column is to be matched with an appropriate entry "a" from the Algorithm column such that the task "t" can be solved using the algorithm "a". Denote such a match as t:a.

Task	Algorithm
T1 - Clustering	A1 - Markov Chain Monte Carlo
T2 - Classification	A2 - K-Medoid
T3 - Sampling	A3 - Linear Discriminant Analysis
T4 - Feature Extraction	A4 - Naive Bayes

Which of the following options is/are the correct matching(s)?

• A. T1:A4, T2:A3, T3:A1, T4:A2
• B. T1:A2, T2:A4, T3:A1, T4:A3
• C. T1:A3, T2:A4, T3:A1, T4:A2
• D. T1:A4, T2:A2, T3:A1, T4:A3

Hint:

K-Medoid clusters, Naive Bayes classifies, MCMC samples, and LDA extracts/reduces features. Match each task to the algorithm built for that exact purpose.

Answer 1

The Correct Option is B

This question checks whether each algorithm listed is correctly paired with the machine learning task it is actually built to solve. Let's look at what each algorithm is used for and eliminate the wrong pairings.

1. A1 - Markov Chain Monte Carlo: this is a sampling technique used to draw samples from probability distributions that are hard to sample from directly, so A1 belongs with T3 (Sampling), not with any other task.
2. A2 - K-Medoid: this algorithm groups data points into clusters around representative medoid points, so A2 belongs with T1 (Clustering), it has no role in classification or feature extraction.
3. A3 - Linear Discriminant Analysis: this technique finds directions that best separate classes and is mainly used to reduce the number of features while keeping class-separating information, so A3 belongs with T4 (Feature Extraction).
4. A4 - Naive Bayes: this is a probabilistic classifier that predicts a class label using Bayes' theorem, so A4 belongs with T2 (Classification).

Putting these correct pairs together gives T1:A2, T2:A4, T3:A1, T4:A3, which is exactly option (B). Every other option swaps at least one algorithm into the wrong task, for instance putting Naive Bayes against Clustering or K-Medoid against Classification, both of which contradict what these algorithms are designed to do.

Let's summarize:

• Clustering pairs with K-Medoid, Classification pairs with Naive Bayes.
• Sampling pairs with MCMC, Feature Extraction pairs with LDA.

So the only fully correct matching is option (B).