The correct answer is: B. ii and iii
Models which overfit are more likely to have high variance and low bias.
Bias is the difference between the expected value of the model’s predictions and the true value. Variance is the spread of the model’s predictions around the expected value.
A model with high bias will tend to make the same mistake over and over again, while a model with high variance will make different mistakes each time.
Overfitting occurs when a model learns the training data too well and is not able to generalize to new data. This can happen when the model is too complex or when the training data is too small.
When a model overfits, it will have high variance and low bias. This is because the model will have learned the noise in the training data and will not be able to generalize to new data.
To avoid overfitting, it is important to use a model that is not too complex and to train the model on a large enough dataset.
Here is a more detailed explanation of each option:
- Option (i): Models which overfit are more likely to have high bias.
This is not true. Models which overfit are more likely to have high variance and low bias.
- Option (ii): Models which overfit are more likely to have low bias.
This is true. Models which overfit are more likely to have low bias.
- Option (iii): Models which overfit are more likely to have high variance.
This is true. Models which overfit are more likely to have high variance.
- Option (iv): Models which overfit are more likely to have low variance.
This is not true. Models which overfit are more likely to have high variance.