What is Regression in Machine Learning?
A data scientist has a dataset containing information about houses: size (sq ft), number of bedrooms, location, and the actual sale price. The goal is to train a model that predicts the price of a new house based on these features. Which type of machine learning task is this?
Quick Answer
The answer is regression, because the goal is to predict a continuous numeric value—the sale price—based on input features like size, bedrooms, and location. Regression models learn the relationship between these independent variables and a dependent variable to output a real number, making it the correct choice for continuous price prediction. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your ability to distinguish regression from classification or clustering; a common trap is confusing price prediction with classification when the output is a dollar amount rather than a category. Remember that any task involving forecasting a quantity, such as house price or temperature, is regression, while predicting a label like “expensive” or “cheap” would be classification. A helpful memory tip is to think of “regression” as “returning a real number”—if the answer is a continuous value, it’s regression every time.
⚠ Common exam trap
A common mix-up: candidates confuse regression with classification because both are supervised learning, but regression outputs a continuous number while classification outputs a discrete label.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
B) Regression
This is a regression task because the goal is to predict a continuous numeric value (the sale price) based on input features. Regression models learn the relationship between independent variables (size, bedrooms, location) and a dependent variable (price) to output a real number. In Azure Machine Learning, regression algorithms like Linear Regression, Decision Forest Regression, or Neural Network Regression would be appropriate for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A) Classification
Why it's wrong here
Classification predicts a discrete category, whereas sale price is a continuous numeric value, so the model must minimise regression error. Classification suits predicting a label such as house style or sale/no-sale. Predicting a quantity is regression.
- ✓
B) Regression
Why this is correct
Regression predicts a continuous numeric value, here the sale price, from labelled features such as size, bedrooms and location. Because the target variable is quantitative rather than a discrete class, regression satisfies the supervised learning requirement in the stem, unlike classification which would output categories.
- ✗
C) Clustering
Why it's wrong here
Clustering groups unlabelled records by similarity, discovering structure without a target variable. The dataset includes the actual sale price, a known label to predict. Clustering suits segmentation tasks such as grouping customers by purchasing behaviour, where no outcome column exists.
- ✗
D) Reinforcement Learning
Why it's wrong here
Reinforcement learning learns a policy from reward signals through trial-and-error interaction with an environment; the dataset supplies labelled examples with no agent or reward. It suits sequential decision problems such as game playing or robot navigation, not predicting a continuous price from fixed features.
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Key term
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Same concept, more angles
1 more way this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is the 'mean absolute error' (MAE) metric used to evaluate in machine learning?
easy- A.The average confidence percentage of classification predictions
- ✓ B.The average absolute difference between regression model predictions and actual values
- C.The proportion of model predictions that deviate from expected values by more than a threshold
- D.How much the model's predictions differ from random chance
Why B: Mean Absolute Error (MAE) is a regression metric that calculates the average of the absolute differences between predicted and actual values. It measures how close predictions are to the true outcomes, with lower values indicating better model accuracy. In Azure Machine Learning, MAE is commonly used to evaluate regression models like linear regression or decision forests.
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Senior Network & Security Engineer · founder of Courseiva
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