A company wants to develop ML applications to improve business operations and efficiency. Select the correct ML paradigm from the following list for each use case. K-means clusteri... A company wants to develop ML applications to improve business operations and efficiency. Select the correct ML paradigm from the following list for each use case. K-means clustering | Supervised learning; Dimensionality reduction | Unsupervised learning; Binary classification | Unsupervised learning; Multi-class classification | Supervised learning.

Understand the Problem

The question is asking us to match different machine learning paradigms with their corresponding use cases. We need to identify which of the provided paradigms (K-means clustering, Dimensionality reduction, Binary classification, and Multi-class classification) fit into supervised or unsupervised learning categories.

Answer

K-means clustering: Unsupervised, Dimensionality reduction: Unsupervised, Binary classification: Supervised, Multi-class classification: Supervised.

K-means clustering | Unsupervised learning; Dimensionality reduction | Unsupervised learning; Binary classification | Supervised learning; Multi-class classification | Supervised learning.

Answer for screen readers

K-means clustering | Unsupervised learning; Dimensionality reduction | Unsupervised learning; Binary classification | Supervised learning; Multi-class classification | Supervised learning.

More Information

In machine learning, clustering and dimensionality reduction are typical unsupervised learning tasks where the model finds patterns or simplifies data without labeled outcomes. Classification, whether binary or multi-class, is a supervised learning task where models predict a category based on input features.

Tips

A common mistake is confusing clustering with classification; clustering groups data without labels, while classification predicts pre-defined categories.

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