MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset and finds that the target variable has a bimodal distribution. Which preprocessing step is most appropriate before modeling?
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
✓
Consider clustering to separate the two modes and model them separately.
Bimodal distributions indicate two distinct underlying groups. Clustering can separate the modes, allowing separate models for each cluster, which often improves performance. Option A is incorrect because standardizing does not change distribution shape. Option B is incorrect because removing outliers would not address bimodality; outliers are extreme values, not necessarily related to modes. Option D is incorrect because log transformation is for skewed unimodal distributions, not bimodal.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardize the target variable to have mean 0 and variance 1.
Why it's wrong here
Standardization does not change distribution shape.
- ✗
Remove outliers from the target variable.
Why it's wrong here
Outliers are not the cause of bimodality.
- ✓
Consider clustering to separate the two modes and model them separately.
Why this is correct
Bimodal distribution may indicate two subpopulations.
- ✗
Apply a log transformation to the target variable.
Why it's wrong here
Log transformation is for unimodal skewed distributions.
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