A data scientist reports that an AI model for customer churn prediction achieves 89% accuracy on the training set but only 67% on an independent holdout set. Which model risk does this MOST indicate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because a large gap between training performance (89%) and holdout performance (67%) is the classic signature of overfitting: the model has learned noise and idiosyncratic patterns in the training data that do not generalize. Data drift and concept drift (A, D) would manifest as declining performance after deployment, not in a train-vs-holdout comparison conducted before deployment.
Full explanation below image
Full Explanation
B is correct because a large gap between training performance (89%) and holdout performance (67%) is the classic signature of overfitting: the model has learned noise and idiosyncratic patterns in the training data that do not generalize. Data drift and concept drift (A, D) would manifest as declining performance after deployment, not in a train-vs-holdout comparison conducted before deployment. Underfitting (C) would show poor performance on both training and holdout sets.