Which statement correctly distinguishes overfitting from underfitting?

Options

  • A. Overfitting occurs when a model learns training data too closely, while underfitting occurs when a model is too simple to capture important patterns
  • B. Overfitting means the model cannot learn anything from training data
  • C. Underfitting always produces perfect testing accuracy
  • D. Overfitting and underfitting have exactly the same meaning
  • E. None of the above

Correct Answer (Detailed Explanation is Below)

A. Overfitting occurs when a model learns training data too closely, while underfitting occurs when a model is too simple to capture important patterns

Detailed Explanation

Overfitting occurs when a model learns the training data, including noise or unnecessary details, too closely and therefore performs poorly on unseen data. Underfitting occurs when the model is too simple or insufficiently trained to capture important patterns in the data.

← Previous Next →

Attempt Quiz Now:

AI and ML Quiz practice set

Feature Posts