There are a couple of drawbacks of a linear model:
- A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
- A linear model can’t be used for discrete or binary outcomes.
- You can’t vary the model flexibility of a linear model.