A salmon hatchery needs one number: next month's returning-adult count. Planners will use water temperature, release year, and river flow. They have no high-or-low return labels, and they are not trying to group rivers into unnamed segments. Which technique should they use?
Select an answer to reveal the explanation.
Short Explanation
Think of it like asking for the temperature, not "hot or cold" and not "which pile does this river belong in." A returning-adult count is a single number on a number line, and that is what regression is built to spit out. Classification wants named buckets, clustering wants unnamed groups, and reinforcement learning wants a score after each move — none of those is next month's headcount.
Full Explanation
Regression predicts a continuous numeric target, which is exactly what a returning-adult count is. Classification would require predefined labels such as high return versus low return, and the hatchery did not create those classes. Clustering would form unlabeled groups of similar rivers, which the team said it does not want. Reinforcement learning trains an agent with rewards and is not used to estimate a single historical-style forecast from sensor and release features.