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 and Infographic
Here's the deal — b is correct because 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. A is wrong because underfitting shows high error on both training and validation sets.
Full explanation below image
Full Explanation
B is correct because 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. A is wrong because underfitting shows high error on both training and validation sets. C is wrong because the loss function choice affects optimization direction, not this specific divergence pattern. D is wrong because ensemble averaging typically reduces variance and helps validation performance.