DA0-002 Data Analysis Practice Question
Which TWO of the following are examples of supervised learning algorithms?
⚠ Common exam trap
CompTIA often tests the distinction between supervised and unsupervised learning by including clustering (K-means) and association (Apriori) as distractors, which candidates mistakenly think are supervised because they involve pattern discovery.
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 supervised learning algorithm because it learns a mapping from input features to a continuous target variable using labeled training data. The model minimizes the difference between predicted and actual values (e.g., via ordinary least squares) to make predictions on new data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Linear regression
Why this is correct
Supervised regression algorithm.
- ✗
K-means clustering
Why it's wrong here
Unsupervised learning.
- ✗
Principal component analysis (PCA)
Why it's wrong here
Unsupervised dimensionality reduction.
- ✓
Decision trees
Why this is correct
Supervised classification/regression.
- ✗
Apriori algorithm
Why it's wrong here
Unsupervised association rule mining.
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