IBM Data Science Practice Test

What reflects the risk of overfitting in machine learning models?

1. Great performance on unseen data.

2. Good training data performance.

3. High precision rates.

4. Models not generalizing to new data.

The correct answer is: Models not generalizing to new data.

Overfitting in machine learning occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers, leading to excellent performance on the training set but poor performance on unseen or new data. The hallmark of overfitting is when a model fails to generalize, meaning that it does not perform well outside of the training dataset. When a model has overfitted, it becomes highly specialized to the training data, capturing specific details that do not apply to broader data distributions. Consequently, this lack of generalization to new data indicates that the model may have learned too much from its training set without grasping the more general features needed for broader applications. Therefore, the reflection of overfitting is demonstrated through a model that performs poorly with new, unseen data, confirming that it has not learned the essential patterns needed for effective predictions in other contexts. The other options either relate to excellent performance indicators or specific metrics that do not directly indicate overfitting characteristics.