Quick Answer
The answer is linear regression, decision tree, and K-nearest neighbors. These three are common machine learning algorithms used for regression because each models a continuous numeric output from input features, though they do so through different mechanisms: linear regression fits a linear equation to minimize the sum of squared errors, decision trees partition data into branches based on feature thresholds to predict averages, and K-nearest neighbors calculates the mean of the target values from the K closest training points. On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish regression algorithms from classification or clustering methods—a common trap is confusing K-nearest neighbors as only a classifier, but it works for regression too when averaging neighbors. A helpful memory tip: think of the three R’s—Regression Relies on Linear, Tree, and Neighbor.
This AI0-001 practice question tests your understanding of ai concepts and foundations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE are common machine learning algorithms used for regression?
Logistic regression
Why wrong: Incorrect; logistic regression is for binary classification.
K-means
Why wrong: Incorrect; k-means is for clustering, not regression.
Linear regression
Correct; linear regression predicts a continuous target.
Decision tree
Correct; decision trees can be used for regression (regression trees).
K-nearest neighbors
Correct; KNN can predict continuous values by averaging neighbors.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Linear regression
Linear regression is a fundamental supervised learning algorithm used for regression tasks, where the goal is to predict a continuous numeric output based on one or more input features. It models the relationship between the dependent and independent variables by fitting a linear equation to the observed data, making it a core algorithm for regression problems.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Logistic regression
Why it's wrong here
Incorrect; logistic regression is for binary classification.
K-means
Why it's wrong here
Incorrect; k-means is for clustering, not regression.
Linear regression
Why this is correct
Correct; linear regression predicts a continuous target.
Related concept
Read the scenario before looking for a memorised answer.
Decision tree
Why this is correct
Correct; decision trees can be used for regression (regression trees).
Related concept
Read the scenario before looking for a memorised answer.
K-nearest neighbors
Why this is correct
Correct; KNN can predict continuous values by averaging neighbors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
CompTIA often tests the distinction between regression and classification algorithms, and the trap here is that candidates mistakenly associate 'logistic regression' with regression tasks due to its name, when it is actually a classification algorithm.
Detailed technical explanation
Under the hood, linear regression minimizes the sum of squared residuals (ordinary least squares) to find the best-fit line, often solved via the normal equation or gradient descent. Decision trees for regression (regression trees) partition the feature space into regions and predict the mean target value of training samples in each leaf, making them non-parametric and capable of capturing non-linear relationships. K-nearest neighbors for regression averages the target values of the K closest training points in feature space, relying on a distance metric (e.g., Euclidean) and requiring careful tuning of K and feature scaling.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
What to study next
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FAQ
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
The correct answer is: Linear regression — Linear regression is a fundamental supervised learning algorithm used for regression tasks, where the goal is to predict a continuous numeric output based on one or more input features. It models the relationship between the dependent and independent variables by fitting a linear equation to the observed data, making it a core algorithm for regression problems.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.
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