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A company is training an ML model to predict customer churn. The training dataset has very few records for customers younger than 25 and older than 70. A data scientist suggests removing the age feature to simplify the model. What will be the result of removing the age feature?
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Removing the age feature doesn't solve the underlying problem of imbalanced representation. Without age data, the model loses important demographic information and will be unable to accurately predict outcomes for underrepresented groups (younger and older customers), as it cannot account for age-related patterns in the data.
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