MLS-C01 Exploratory Data Analysis Practice Question
Which THREE of the following are valid techniques for detecting outliers in a dataset during exploratory data analysis? (Select 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
✓
Z-score method: flag points with absolute Z-score > 3.
Z-score, IQR, and Isolation Forest are all common outlier detection methods. Option B (Linear regression) is not for outlier detection; it models relationships between variables. Option D (K-means) is a clustering algorithm, not primarily for outlier detection.
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 method: flag points with absolute Z-score > 3.
Why this is correct
Z-score is a standard outlier detection technique.
- ✗
Linear regression residuals.
Why it's wrong here
Linear regression residuals are used to check model fit, not directly for outlier detection in EDA.
- ✓
Isolation Forest algorithm.
Why this is correct
Isolation Forest is an ensemble method for anomaly detection.
- ✗
K-means clustering.
Why it's wrong here
K-means is for clustering, not specifically for outlier detection.
- ✓
Interquartile Range (IQR) method: flag points outside 1.5*IQR from quartiles.
Why this is correct
IQR is a common non-parametric method.
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