Quizzes: overfitting, underfitting

Quiz 1: Overfitting

Question 1: What is overfitting in machine learning?
A) When a model performs well on training data but poorly on new, unseen data
B) When a model performs poorly on both training and test data
C) When a model performs well on test data but poorly on training data
D) When a model has too few parameters

Question 2: Which of the following is a common cause of overfitting?
A) Too little training data
B) Too many features or too complex a model
C) Low learning rate
D) Incorrect loss function

Quiz 2: Underfitting

Question 1: What is underfitting in machine learning?
A) When a model performs well on training data but poorly on test data
B) When a model performs poorly on both training and test data
C) When a model performs well on test data but poorly on training data
D) When a model has too many parameters

Question 2: Which of the following is a common cause of underfitting?
A) A model that is too simple
B) Too much training data
C) High learning rate
D) Incorrect evaluation metric

Question 3: Which technique can help reduce underfitting?
A) Simplifying the model
B) Reducing the number of features
C) Increasing the complexity of the model
D) Reducing the number of epochs