Which of the following statements is false?
A) Overfitting can occur when your model is too simple compared to what it is modeling-in the most extreme overfitting case, a model memorizes its training data.
B) When you make predictions with an overfit model, they will be accurate if new data matches the training data, but the model could perform poorly with data it has never seen.
C) Overfitting tends to occur in deep learning as the dimensionality of the layers becomes too large.
D) Some techniques to prevent overfitting include training for fewer epochs, xe "data augmentation"data augmentation, xe "dropout"dropout and xe "L1 regularization"L1 or xe "regularization"xe "L2 regularization"L2 regularization.

Question

Quizplus · Accepted Answer

Overfitting occurs when a model is too complex relative to the simplicity or noise within the training data, not too simple. It happens when the model learns the noise in the training data to the extent that it negatively impacts the performance of the model on new data.

Which of the Following Statements Is False