Question

Which of the following are true about bias and variance of overfitted and underfitted models? (multiple options may be correct)
Underfitted models have high bias.
Underfitted models have low bias.
Overfitted models have high variance.
Overfitted models have low variance.
none of these

Answer 1

● Of the given options the underfitted models have high bias is true.

● An underfitted model is where a few parameters or terms that would show up in an accurately determined model are absent.

● Underfitting would happen, for instance, when fitting a straight model to non-direct information. Such a model will in general have poor prescient execution.

Answer 2

Answer:

Underfitted models have high bias.

Overfitted models have high variance.

Explanation:

1. ) Underfitting happens when a model unable to capture the underlying pattern of the data. These models usually have high bias and low variance. It happens when we have very less amount of data to build an accurate model or when we try to build a linear model with a nonlinear data. Also, these kinds of models are very simple to capture the complex patterns in data like Linear and logistic regression.

2) Overfitting happens when  a model captures the noise along with the underlying pattern in data. It happens when we train our model a lot over noisy dataset. These models have low bias and high variance. These models are very complex like Decision trees which are prone to overfitting.