Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model. i. Models which overfit are more likely to have high bias ii. Models which overfit are more likely to have low bias iii. Models which overfit are more likely to have high variance iv. Models which overfit are more likely to have low variance

Answer: Option C

i. False. Models that overfit tend to have low bias because they capture the training data's noise and details, leading to a smaller bias towards the training set.
ii. False. As mentioned in (i), models that overfit typically have low bias, not high bias.
iii. True. Overfitting often leads to high variance because the model captures noise in the training data, resulting in a model that performs well on the training set but poorly on unseen data.
iv. True. Overfitting is characterized by capturing noise and spurious patterns from the training data, leading to low variance since the model's predictions are tightly fitted to the training data.
Therefore, Option C is correct as it includes the true statements iii and iv.