BenchMark Quizzes by MDFT Pro

Select Machine Learning Type For Numeric Prediction

Claire, the Enrollment Forecasting Analyst at MDFT Pro, is developing predictive models to help the company plan course capacity and resource allocation for upcoming training programs. MDFT Pro, a well-known training agency, needs to predict student enrollment numbers for new certification courses based on factors like course difficulty, market demand, pricing, instructor reputation, and seasonal trends. Claire wants to build a machine learning model that can forecast the expected number of students who will enroll in each course offering, enabling the company to optimize classroom sizes, instructor assignments, and learning material quantities before course launches.

Which type of machine learning should Claire use to predict the numeric quantity of student enrollments for course planning?

Choose the correct answer from the options below.

Explanations for each answer:

Classification is incorrect. Classification predicts discrete categories or classes, not continuous numeric values like enrollment numbers or population sizes.
Regression is correct. Regression is designed to predict continuous numeric values, making it appropriate for forecasting quantities like student enrollment numbers or capacity planning.
Clustering is incorrect. Clustering groups similar items together but doesn't predict numeric values or quantities for planning and forecasting purposes.
Time series forecasting is incorrect. Time series forecasting predicts values over time using historical patterns, which isn't necessary for single-point enrollment predictions based on program characteristics.

Learn more about regression:

Regression Models

Exam Preparation Quiz

Select Machine Learning Type For Numeric Prediction