MLS-C01 Modeling Practice Question
Which TWO of the following are examples of unsupervised learning tasks?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between supervised and unsupervised learning by presenting tasks that seem intuitive (like clustering) but pairing them with tasks that require labeled outputs (like classification or regression), so candidates must recognize that any task involving a target variable is supervised.
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
✓
Dimensionality reduction using PCA
Principal Component Analysis (PCA) is an unsupervised learning technique used for dimensionality reduction. It works by identifying the directions (principal components) that maximize variance in the data, without requiring any labeled target variable. This makes it a classic example of unsupervised learning, as the algorithm learns patterns solely from the input features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Classifying emails as spam or not spam
Why it's wrong here
Classification is supervised.
- ✓
Dimensionality reduction using PCA
Why this is correct
PCA reduces features without labels.
- ✗
Sentiment analysis of product reviews
Why it's wrong here
Sentiment analysis is supervised.
- ✓
Clustering customer segments
Why this is correct
Clustering groups data without labels.
- ✗
Predicting house prices
Why it's wrong here
Regression is supervised.
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