True or false: Value ranges for all attributes for every observation in a scoring data set must be within the value ranges for the corresponding attributes in the training data set in a linear regression model.

• A. True
• B. False
• C. Sometimes
• D. Not Necessarily

Answer: (A)

In linear regression, the model learns the relationships between features and the outcome from the data it was trained on. If you score new observations with feature values outside the ranges seen during training, you’re asking the model to extrapolate beyond what it learned, which can lead to unreliable predictions. Keeping scoring data within the training ranges helps maintain prediction reliability and ensures that any scaling or normalization applied using training data stays appropriate. If staying within range isn’t possible, steps such as retraining with broader data, clipping values to the training min/max, or choosing models that handle extrapolation more gracefully should be considered.