MLS-C01 Modeling Practice Question
A data scientist is training a k-means clustering model on a dataset with 1,000 points. The scientist uses the elbow method to choose the number of clusters. The elbow plot shows a clear bend at k=4. After running k-means with k=4, the scientist wants to evaluate the quality of the clustering. Which THREE of the following are suitable internal clustering validation metrics? (Choose THREE.)
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
✓
Calinski-Harabasz index
Silhouette score, Davies-Bouldin index, and Calinski-Harabasz index are all internal validation metrics that do not require ground truth labels. They measure compactness and separation. Rand index and adjusted Rand index require ground truth labels (external validation).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Adjusted Rand index
Why it's wrong here
Also requires ground truth labels.
- ✗
Rand index
Why it's wrong here
Requires ground truth labels; external validation metric.
- ✓
Calinski-Harabasz index
Why this is correct
Ratio of between-cluster variance to within-cluster variance; higher is better.
- ✓
Silhouette score
Why this is correct
Measures how similar a point is to its own cluster compared to other clusters.
- ✓
Davies-Bouldin index
Why this is correct
Ratio of within-cluster distances to between-cluster distances; lower is better.
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