A data scientist is training a gradient boosting model in SAS and notices the training error continues to decrease while validation error increases after a certain number of iterations. Which concept best describes this phenomenon?
Select an answer to reveal the explanation.
Short Explanation
You're seeing overfitting when training error keeps dropping but validation error climbs. The model is memorizing noise in the training data instead of learning patterns that generalize.
Full Explanation
The correct concept is overfitting due to excessive model complexity relative to the training data. When training error decreases while validation error increases, the model is memorizing training data noise rather than learning generalizable patterns — the classic definition of overfitting. Underfitting due to insufficient model complexity is wrong because underfitting shows high error on both training and validation sets. High bias caused by a poorly specified loss function is wrong because the loss function choice affects optimization direction, not this specific divergence pattern. Variance reduction caused by ensemble averaging is wrong because ensemble averaging typically reduces variance and helps validation performance.