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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #163
You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorica
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Submitted by akirajp· Apr 18, 2026Data processing and feature engineering
Question
You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?
Options
- AUse sparse representation in the test set.
- BRandomly redistribute the data, with 70% for the training set and 30% for the test set
- CApply one-hot encoding on the categorical variables in the test data
- DCollect more data representing all categories
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Topics
#Data preprocessing#Categorical variables#One-hot encoding#Training-test consistency