MLA-C01 · Question #148
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the responses into a…
The correct answer is A. Binary encoding B. Label encoding. Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and…
Question
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either “yes” or “no.” The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset. Which methods will meet these requirements? (Choose two.)
Options
- ABinary encoding
- BLabel encoding
- COne-hot encoding
- DStatistical imputation
- ETokenization
How the community answered
(35 responses)- A71% (25)
- C6% (2)
- D14% (5)
- E9% (3)
Explanation
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.
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