MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with numerical features and a binary target variable. The data scientist creates a pairplot and notices that one feature has a bimodal distribution when colored by the target class. What does this observation suggest?
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
✓
The feature is likely predictive of the target.
A bimodal distribution separated by class indicates the feature can help distinguish between the classes, making it predictive. Option A is wrong because bimodality separated by class suggests the feature is useful, not irrelevant. Option C is wrong because bimodality is not an indication of outliers; it shows a pattern related to the target. Option D is wrong because bimodality does not imply missing values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The feature is irrelevant and should be removed.
Why it's wrong here
A bimodal distribution by class suggests the feature has predictive power.
- ✓
The feature is likely predictive of the target.
Why this is correct
Different distributions for each class indicate the feature can separate the classes.
- ✗
The feature contains outliers that need to be removed.
Why it's wrong here
Bimodality is a property of the distribution, not necessarily outliers.
- ✗
The feature has missing values that need to be imputed.
Why it's wrong here
Bimodality is not caused by missing values.
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