MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA and observes that a feature 'purchase_amount' has many zeros and a long tail of positive values. What type of model would be appropriate for this target variable?
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
✓
Zero-inflated negative binomial regression.
Zero-inflated negative binomial regression models are designed for count data with a high frequency of zeros, which matches the 'purchase_amount' feature having many zeros and a long tail of positive values. Option B is incorrect: Log transformation does not handle the zero-inflation problem; zeros become undefined or need adjustment. Option C is incorrect: Logistic regression is for binary outcomes, not continuous or count data. Option D is incorrect: Poisson regression accommodates count data but assumes the variance equals the mean and does not handle excess zeros; zero-inflation violates this assumption.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Zero-inflated negative binomial regression.
Why this is correct
Zero-inflated negative binomial regression handles both the excess zeros and the overdispersion common in such data.
- ✗
Linear regression after log transformation.
Why it's wrong here
Linear regression after log transformation assumes normality of errors and cannot handle zeros directly (log(0) is undefined).
- ✗
Logistic regression on binary indicator of purchase.
Why it's wrong here
Logistic regression models a binary outcome, but 'purchase_amount' is not binary.
- ✗
Poisson regression.
Why it's wrong here
Poisson regression assumes equal mean and variance and does not account for the high proportion of zeros, leading to biased estimates.
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