IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

Supervised learning and unsupervised learning are fundamental concepts in machine learning that are distinguished primarily by the nature of the data used and the intended outcomes.

In supervised learning, algorithms are trained using labeled data, meaning that each training example includes input data paired with the correct output. This training allows the model to learn the relationship between inputs and outputs, enabling it to make predictions or classifications based on new, unseen data. For example, in a supervised learning task such as email classification, each email is labeled as "spam" or "not spam," guiding the algorithm in learning the patterns that distinguish between the two classes.

In contrast, unsupervised learning does not involve labeled data. Instead, it aims to uncover hidden patterns or groupings within the data itself without predefined outcomes. When using unsupervised learning, the algorithm must identify similarities and differences in the data to form clusters or derive insights. An example of this would be customer segmentation, where the model analyzes purchasing behavior without predefined categories to group customers into segments.

While computational power may vary depending on the complexity and size of the dataset, it is not a defining characteristic that distinguishes the two learning paradigms. Additionally, unsupervised learning explicitly does not require labeled data, further emphasizing the distinction