MLS-C01 Exploratory Data Analysis Practice Question
Which TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that removing values more than one standard deviation from the mean is a valid outlier detection technique, when in fact it removes a large portion of normal data and is not a standard practice.
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 Z-score and flag values with absolute Z-score > 3.
The Z-score method (Option B) is a standard statistical technique for detecting outliers in a univariate continuous feature. It measures how many standard deviations a data point is from the mean, and flagging values with an absolute Z-score greater than 3 is a common threshold because, under a normal distribution, approximately 99.7% of data falls within three standard deviations, making points beyond this likely outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply a Random Forest classifier to predict outliers.
Why it's wrong here
Outlier detection is unsupervised; Random Forest requires labels.
- ✓
Use Z-score and flag values with absolute Z-score > 3.
Why this is correct
Z-score >3 is a common outlier threshold.
- ✗
Remove any value that is more than one standard deviation from the mean.
Why it's wrong here
One standard deviation includes 68% of data; too aggressive.
- ✗
Use DBSCAN clustering with default parameters.
Why it's wrong here
DBSCAN is for multivariate data and requires parameter tuning.
- ✓
Use the interquartile range (IQR) and flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.
Why this is correct
IQR method is standard for univariate outlier detection.
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