MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset containing 10,000 observations and 100 features. The scientist wants to detect outliers in the dataset. Which method is most appropriate for outlier detection in a high-dimensional space?
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
✓
Use Isolation Forest
Isolation Forest is the most appropriate method for outlier detection in high-dimensional space because it isolates anomalies by randomly splitting features, making it effective for high-dimensional data without assuming any underlying distribution. Option A is wrong because Z-score assumes normality and is univariate, unsuitable for high-dimensional data. Option C is wrong because Mahalanobis distance assumes multivariate normality and can be computationally expensive and sensitive to high dimensionality. Option D is wrong because IQR is univariate and does not capture interactions between features in high-dimensional spaces.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Z-score to identify points beyond 3 standard deviations
Why it's wrong here
Z-score is univariate and assumes normal distribution.
- ✓
Use Isolation Forest
Why this is correct
Isolation Forest is designed for high-dimensional data and does not assume distribution.
- ✗
Use Mahalanobis distance
Why it's wrong here
Mahalanobis distance assumes multivariate normality and can be unstable in high dimensions.
- ✗
Use interquartile range (IQR) for each feature
Why it's wrong here
IQR is univariate and ignores correlations.
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