A machine-learning algorithm considers the height, weight, and bone density of 100 people. From these descriptors, it predicts the exact age when each person will retire. Which word best describes this application of machine learning?

Question

BlackTom AI · Accepted Answer

Here we evaluate what the machine-learning task is actually doing with the given descriptors.
Option: Iteration. This term refers to repeating a process to refine results, not the type of prediction being made. It does not describe predicting a numeric value like retirement age directly.
Option: Recursion. Recursion involves a function calling itself to solve a problem, which is a programming or algorithmic technique rather than the problem type in supervised learning.
Option: Classification. Classification assigns inputs to discrete categories (e.g., yes/no, dog/cat). Since retirement age is a continuous number, this option models outcomes as distinct classes rather than a numeric regression.
Option: Segmentation. In ML, segmentation often means dividing data into meaningful groups or segments, frequently used in image processing or clustering contexts. It does not imply predicting a precise numerical age.
Option: Regression. This is the appropriate category when the target variable is continuous, such as predicting an exact retirement age from height, weight, and bone density. The model outputs a real-valued number, aligning with regression tasks.

A machine-learning algorithm considers the height, weight, and bone density of 100 people. From these descriptors, it predicts the exact age when each person will retire. Which word best describes this application of machine learning?单项选择题

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