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

Step 1: Recall what each algorithm actually does.
K-Medoid is a partitioning algorithm that groups similar data points into clusters around representative "medoid" points, it is a clustering method.
Naive Bayes is a probabilistic classifier that assigns a class label to a data point using Bayes' theorem with a "naive" independence assumption between features, it is a classification method.
Markov Chain Monte Carlo (MCMC) is a family of algorithms used to draw samples from a complicated probability distribution when direct sampling is hard, it is a sampling method.
Linear Discriminant Analysis (LDA) projects data onto a lower-dimensional space chosen to best separate classes, it is primarily used as a feature extraction/dimensionality reduction method (though it can also assist classification).

Step 2: Match each task to the algorithm designed for it.
T1 - Clustering needs an algorithm that groups unlabeled data by similarity, this is K-Medoid (A2). So T1:A2.
T2 - Classification needs an algorithm that assigns class labels, this is Naive Bayes (A4). So T2:A4.
T3 - Sampling needs an algorithm for drawing samples from a distribution, this is MCMC (A1). So T3:A1.
T4 - Feature Extraction needs an algorithm for reducing/transforming features, this is LDA (A3). So T4:A3.

Step 3: Compare with each option.
Option (A), T1:A4 wrongly pairs Clustering with Naive Bayes (a classifier, not a clustering method), so (A) is wrong.
Option (B), T1:A2, T2:A4, T3:A1, T4:A3, matches exactly what we derived in Step 2.
Option (C), T1:A3 wrongly pairs Clustering with LDA (a feature extraction tool, not a clustering method), so (C) is wrong.
Option (D), T2:A2 wrongly pairs Classification with K-Medoid (a clustering method, not a classifier), so (D) is wrong.

Final Answer:
Only option (B) gives the correct matching for all four tasks. \[ \boxed{\text{Option (B): T1:A2, T2:A4, T3:A1, T4:A3}} \]