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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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