BenchMark Quizzes by MDFT Pro

Define Clustering In Machine Learning

Claire, the Student Success Analyst at MDFT Pro, wants to identify different learning patterns among students to improve course design and support services. MDFT Pro, a well-known training agency, has collected extensive data on student engagement metrics, study habits, completion times, and assessment scores, but doesn’t have predefined categories for different types of learners. Claire needs to discover natural groupings in this student data to understand if there are distinct learning profiles, such as visual learners, hands-on learners, or self-paced learners, without having any prior labels or categories defined.

What machine learning approach should Claire use to discover these natural student groupings?

Choose the correct answer from the options below.

Explanations for each answer:

A supervised model for predicting numeric values is incorrect. This describes regression, which uses labeled training data to predict continuous values, not group unlabeled data into clusters.
A supervised model for predicting categories is incorrect. This describes classification, which requires pre-labeled categories and supervised learning, unlike clustering which works with unlabeled data.
An unsupervised model for grouping similar entities is correct. Clustering is an unsupervised technique that discovers hidden patterns by grouping similar data points together without predefined labels or categories.
A deep learning model for pattern recognition is incorrect. While deep learning can be used for clustering, this definition is too narrow and doesn't capture the fundamental unsupervised grouping nature of clustering.

Learn more about clustering:

Machine Learning Clustering

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Define Clustering In Machine Learning