A model validation team challenges a model based on finding that its training error is significantly lower than validation error. What model risk does this finding indicate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because a large gap between low training error and high validation error is the classic signature of overfitting — the model has memorized the training data including its noise and idiosyncrasies but lacks the ability to generalize to unseen data. A produces both high training and high validation error.
Full explanation below image
Full Explanation
B is correct because a large gap between low training error and high validation error is the classic signature of overfitting — the model has memorized the training data including its noise and idiosyncrasies but lacks the ability to generalize to unseen data. A produces both high training and high validation error. C would typically cause validation error to be unrealistically low. D affects validation metrics but does not explain the training-validation gap by itself.