In logistic regression, the score values must lie within the lower and upper bounds defined by the training data.

• A. Always Between 0 And 1
• B. Unbounded
• C. Always Equal To Training Data Values
• D. Constrained By The Lower And Upper Bounds Defined By The Training Data

Answer: (D)

Scores in logistic regression are probabilities produced by applying the logistic function to a linear score. This guarantees they fall between 0 and 1 for any input, because a probability cannot be less than 0 or greater than 1. Those bounds reflect the outcomes we observe in the training data, which are binary (0 or 1), setting the natural limits of the predicted probability. So the score values are constrained by the lower bound 0 and the upper bound 1 defined by the training data. The idea captures why the predictions cannot wander outside this interval, whereas saying they are unbounded or always equal to training values would not match how logistic regression generates scores.