MLS-C01 · Question #69
MLS-C01 Question #69: Real Exam Question with Answer & Explanation
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Question
A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field. For this use case, which is the most effective course of action to address test data samples with missing features?
Options
- ADrop the test samples with missing full review text fields, and then run through the test set.
- BCopy the summary text fields and use them to fill in the missing full review text fields, and then
- CUse an algorithm that handles missing data better than decision trees.
- DGenerate synthetic data to fill in the fields that are missing data, and then run through the test set.
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