AI0-001 AI Concepts and Foundations Practice Question
Which THREE are common machine learning algorithms used for regression?
⚠ Common exam trap
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.
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
✓
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.
Answer analysis
Option-by-option breakdown
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.
- ✓
Decision tree
Why this is correct
Correct; decision trees can be used for regression (regression trees).
- ✓
K-nearest neighbors
Why this is correct
Correct; KNN can predict continuous values by averaging neighbors.
About these practice questions
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.