True or false: In the logistic regression model, the scoring values must all fall within the lower and upper bounds set by the corresponding values in the training data set.

• A. True
• B. False
• C. Only for Probabilities
• D. Lower And Upper Bounds Defined By Training Data Must Be Respected

Answer: (D)

The idea being tested is how model predictions relate to the data the model was trained on. In logistic regression, the score you obtain comes from the patterns learned during training, so it’s tied to the region of feature space that was observed there. If you apply the model to inputs that lie outside that region, you’re effectively extrapolating, and the reliability of the scores can degrade. Therefore, a practical guideline is to respect the lower and upper bounds defined by the training data when interpreting or deploying these scores. That makes the statement about bounds being respected the most appropriate answer. The other options are less precise: claiming it’s always true is too strong; saying it’s only about probabilities misses the broader issue of relying on the training data’s domain; and the last option is a restatement that doesn’t capture the practical emphasis on staying within the training-data-defined range.