A thrift warehouse has thousands of donation photos and no category tags. The team wants the system to discover groups of visually similar items so staff can invent aisle names later. They are not training a reward-seeking robot. Which learning approach fits?
Select an answer to reveal the explanation.
Short Explanation
Think of dumping a pile of photos on a table and asking someone to make stacks of things that look alike, with no name tags yet. That is unsupervised learning: no target label on each donation. A filename is not a category, and nobody is handing out treats to a robot.
Full Explanation
Unsupervised learning finds structure in examples that have no target label, which is grouping untagged donation photos. A photo file does not secretly include a category label. Reinforcement learning trains an agent with rewards and is not this grouping job. Real-time inference is a way to score an already trained model; it does not create labels or replace unsupervised learning.