Overfitting occurs when a model learns noise from the training data.

Overfitting occurs when a model learns noise from the training data.

• A. False
• B. Only occurs with small datasets
• C. Regularization worsens overfitting.
• D. True

Answer: (D)

Overfitting happens when a model learns noise and random fluctuations in the training data instead of the true underlying patterns. When the model is allowed to be too complex or trained too long, it can fit these idiosyncrasies, giving excellent performance on the training set but poor generalization to new data because those noisy details aren’t present there. The statement is true for this reason. It’s not restricted to small datasets; overfitting can occur whenever the model’s capacity exceeds the amount of meaningful signal in the data, even with large datasets. Regularization and other controls reduce overfitting by limiting model complexity, improving generalization. The other options aren’t correct: overfitting isn’t limited to small datasets, and regularization helps—not worsens—overfitting.

Overfitting happens when a model learns noise and random fluctuations in the training data instead of the true underlying patterns. When the model is allowed to be too complex or trained too long, it can fit these idiosyncrasies, giving excellent performance on the training set but poor generalization to new data because those noisy details aren’t present there. The statement is true for this reason.

It’s not restricted to small datasets; overfitting can occur whenever the model’s capacity exceeds the amount of meaningful signal in the data, even with large datasets. Regularization and other controls reduce overfitting by limiting model complexity, improving generalization. The other options aren’t correct: overfitting isn’t limited to small datasets, and regularization helps—not worsens—overfitting.