DA0-002 Data Analysis Practice Question
A data scientist applies K-means clustering to a customer dataset. The elbow method suggests using 4 clusters. After running K-means with k=4, the within-cluster sum of squares (WCSS) is plotted against k, and the elbow is at k=4. What does this indicate?
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
✓
Increasing k beyond 4 would not significantly reduce WCSS.
The elbow method suggests that increasing k beyond 4 yields diminishing returns in reducing WCSS; k=4 is a good trade-off.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increasing k beyond 4 would not significantly reduce WCSS.
Why this is correct
The elbow point is where the rate of decrease sharply changes.
- ✗
The data naturally forms 4 clusters with no noise.
Why it's wrong here
The elbow method suggests a reasonable k, but doesn't guarantee no noise.
- ✗
The algorithm converged to a local minimum.
Why it's wrong here
Convergence to local minimum is a separate issue.
- ✗
The model has overfit the data.
Why it's wrong here
Overfitting is not directly indicated by the elbow method.
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