A company trained an ML model on Amazon SageMaker to predict customer credit risk. The model shows 90% recall on training data and 40% recall on unseen testing data. Which conclusion can the company…

Question

Options

• AThe model is overfitting on the training data.
• BThe model is underfitting on the training data.
• CThe model has insufficient training data.
• DThe model has insufficient testing data.

• A
  43% (17)
• B
  8% (3)
• C
  18% (7)
• D
  33% (13)

Explanation

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. 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

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