1. What does over-fitting mean in machine learning?

A) The model is generalizing
B) The model is memorizing instead of generalizing
C) The model is adjusting itself slightly to validation data until it is perfect
D) The model is failing to recognize patterns in the data

Answer: B

 1. Can a model over-fit to validation data?

A) Yes, it is possible
B) No, it is not possible
C) It depends on the complexity of the model
D) It depends on the size of the validation set

Answer: A

 3. What can happen if a model over-fits to the validation set?

A) The model will generalize better to new data
B) The model will perform worse on new data
C) The model will perform just as well on new data as on the validation set
D) The model will not be able to predict anything accurately

Answer: B

4. How does a model over-fit to the validation set?

A) By adjusting itself to the training set
B) By generalizing too much to the validation set
C) By memorizing the validation set instead of generalizing
D) By using a complex algorithm

Answer: C
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence