MLS-C01 Exploratory Data Analysis Practice Question

Which THREE techniques are commonly used to detect outliers in a dataset? (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

✓

Interquartile range (IQR)

Options A, D, and E are correct. Z-score and IQR are standard statistical methods for identifying outliers. Isolation Forest is a machine learning algorithm specifically designed for anomaly detection. Option B (k-means clustering) is incorrect because it is a clustering algorithm, not typically used for outlier detection. Option C (PCA) is incorrect because principal component analysis is used for dimensionality reduction, though it can be used in some outlier detection contexts, it is not one of the three most common techniques.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Interquartile range (IQR)
Why this is correct
IQR is a common statistical method to detect outliers by identifying data points beyond 1.5 times the IQR from the quartiles.
✗
k-means clustering
Why it's wrong here
K-means clustering is for partitioning data into clusters, not specifically for outlier detection, so it is incorrect.
✗
Principal component analysis (PCA)
Why it's wrong here
PCA is primarily for dimensionality reduction, not a common outlier detection technique, so it is incorrect.
✓
Z-score
Why this is correct
Z-score is a standard method to identify outliers by measuring how many standard deviations a point is from the mean.
✓
Isolation Forest
Why this is correct
Isolation Forest is a machine learning algorithm designed for anomaly detection, making it a common technique.

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MLS-C01 Exploratory Data Analysis Practice Question

Why each option matters

Option-by-option breakdown

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