Q36. You are working with two variables:

is_local_guide: 1 if the reviewer is a Local Guide, missing otherwise
review_text: text of the review, missing if the user did not leave a comment

Which of the following correctly handles missing values?

Question

Q36. You are working with two variables:

is_local_guide: 1 if the reviewer is a Local Guide, missing otherwise
review_text: text of the review, missing if the user did not leave a comment

Which of the following correctly handles missing values?

BlackTom AI · Accepted Answer

Binary indicator variables and unstructured text must be handled differently: is_local_guide is a binary (1 = Local Guide, missing otherwise) so missing values semantically mean "not a Local Guide" and should be set to 0; review_text is unstructured text, so representing a missing comment as numeric 0 is inappropriate — leave it missing, use an empty string, or encode a separate "has_review" flag.

1) Relevant knowledge points and concepts
- Variable types: categorical/binary vs. unstructured text require different imputation strategies.
- Meaning of missingness: if the data definition states missing implies absence, then impute the absence value (0 for a binary indicator).
- Imputation appropriateness: numeric imputation (mean, 0) only makes sense for numeric variables; text requires different handling (keep missing, empty string, or a separate indicator).
- Bias and data loss: dropping rows with missing values can remove valid and informative records and introduce bias.

2) Step-by-step reasoning
- Inspect is_local_guide: defined as 1 if Local Guide, missing otherwise. Therefore missing = not a Local Guide. Logical imputation: missing -> 0.
- Inspect review_text: this is free text; missing means the user did not leave a comment. Replacing text with 0 would mix data types and falsely imply a numeric value. Better options: keep as missing/NaN, replace with empty string "", or create an additional binary feature has_review = 0/1.

3) Intermediate mapping example
- is_local_guide: {1 -> 1, missing -> 0}
- review_text: {text -> text, missing -> NaN or "" (and optionally has_review = 0)}

4) Why the correct answer is correct
Replacing missing values with 0 in is_local_guide preserves the intended semantics (absence = 0). Not replacing review_text with 0 preserves data type and avoids introducing invalid numeric values; missing text should be encoded differently.

5) Why other options are incorrect (brief)
- Replace missing with 0 in both: wrong because 0 is not a valid representation for text.
- Replace missing with mean in both: mean is meaningless for text; for binary it would introduce fractional/ambiguous values and distort interpretation.
- Drop all rows with any missing: discards valid observations and biases the dataset.
- Treat missing values the same across variables: ignores differing types and meanings of missingness.

Conclusion: The correct strategy is to replace missing values with 0 in is_local_guide, but not in review_text — matching the provided answer.

Q36. You are working with two variables: is_local_guide: 1 if the reviewer is a Local Guide, missing otherwise review_text: text of the review, missing if the user did not leave a comment Which of the following correctly handles missing values?单项选择题

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