Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider? 1. I will add more variables 2. I will start introducing polynomial degree variables 3. I will remove some variables

The correct answer is C. 1 and 3.

Underfitting occurs when a model does not capture the true relationship between the variables. This can happen when the model is too simple or when there is not enough data. To address underfitting, you can add more variables, introduce polynomial degree variables, or use a more complex model.

Adding more variables can help to capture the true relationship between the variables. However, it is important to only add variables that are truly related to the response variable. Otherwise, you may end up overfitting the data.

Introducing polynomial degree variables can also help to capture the true relationship between the variables. This is because polynomials can be used to represent a wide range of relationships. However, it is important to choose the correct degree of polynomial. Otherwise, you may end up overfitting the data.

Removing some variables can also help to address underfitting. This is because removing variables that are not related to the response variable can help to improve the model’s accuracy. However, it is important to only remove variables that are truly not related to the response variable. Otherwise, you may end up overfitting the data.

In conclusion, the correct answer is C. 1 and 3. You can add more variables or introduce polynomial degree variables to address underfitting. However, it is important to only add variables that are truly related to the response variable and to choose the correct degree of polynomial. You can also remove some variables to address underfitting, but it is important to only remove variables that are truly not related to the response variable.