A company trained an ML model on Amazon SageMaker to predict customer credit risk.

The ML model shows 90% recall on training data but only 40% recall on unseen testing data, indicating a significant performance drop. This discrepancy suggests the model has learned the training data too well, including noise and specific patterns that do not generalize to new data, which is a classic sign of overfitting.

Exact Extract from AWS AI Documents:

From the Amazon SageMaker Developer Guide:

"Overfitting occurs when a model performs well on training data but poorly on unseen test data, as it has learned patterns specific to the training set, including noise, that do not generalize. A large gap between training and testing performance metrics, such as recall, is a common indicator of overfitting."

(Source: Amazon SageMaker Developer Guide, Model Evaluation and Overfitting)

Detailed Explanation:

Option A: The model is overfitting on the training data.This is the correct answer. The significant drop in recall from 90% (training) to 40% (testing) indicates the model is overfitting, as it performs well on training data but fails to generalize to unseen data.

Option B: The model is underfitting on the training data.Underfitting occurs when the model performs poorly on both training and testing data due to insufficient learning. With 90% recall on training data, the model is not underfitting.

Option C: The model has insufficient training data.Insufficient training data could lead to poor performance, but the high recall on trainingdata (90%) suggests the model has learned the training data well, pointing to overfitting rather than a lack of data.

Option D: The model has insufficient testing data.Insufficient testing data might lead to unreliable test metrics, but it does not explain the large performance gap between training and testing, which is more indicative of overfitting.