When should data transformation be avoided during the preprocessing of data for machine learning?
- Always
- When working with categorical data
- When the data distribution is already ideal
- When the machine learning model requires it
Data transformation should be avoided when the data distribution is already ideal for the machine learning model being used. In such cases, transforming the data can introduce unnecessary complexity and potentially degrade model performance. In other situations, data transformation might be necessary to make the data suitable for modeling.
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