MLS-C01 Exploratory Data Analysis Practice Question
A data scientist needs to detect outliers in a dataset with multiple features that follow different distributions. Which method is most robust for multivariate outlier detection?
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
✓
Isolation Forest
Isolation Forest is an ensemble method that isolates anomalies effectively in high-dimensional spaces without assuming any specific distribution. Option A is wrong because Z-score assumes a normal distribution. Option B is wrong because IQR is univariate and does not capture multivariate interactions. Option C is wrong because DBSCAN is primarily a clustering algorithm and is not specifically designed for outlier detection, though it can identify outliers as noise; however, Isolation Forest is more robust for this purpose.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Z-score threshold
Why it's wrong here
Incorrect: Z-score assumes normality and is univariate.
- ✗
Interquartile range (IQR)
Why it's wrong here
Incorrect: IQR is univariate and ignores feature interactions.
- ✗
DBSCAN clustering
Why it's wrong here
Incorrect: DBSCAN is primarily for clustering, not outlier detection, though it can identify noise points.
- ✓
Isolation Forest
Why this is correct
Correct: Isolation Forest works well for multivariate data without distributional assumptions.
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