The main disadvantages of linear regression are as follows:
- Assumption of linearity: It assumes that there exists a linear relationship between the independent variables(input) and dependent variables (output), therefore we are not able to fit the complex problems with the help of a linear regression algorithm.
- Outliers: It is sensitive to noise and outliers.
- Multicollinearity: It gets affected by multicollinearity.