Limitations of Regression in Machine Learning

Question

what happens when you want to do regression on a huge dataset? The kernel trick is extremely computationally expensive while the linear regression models are too simplistic as real data is seldom linear

Answer 1

Linear regression makes assumptions that dependent variable is linearly related to independent variable.The residual value can give an account of non-linearity present in the data.Linear regression is sensitive to the outliers and takes into account mean of the dependent variable.
We use Regression analysis based on the following reasons:
It is simple and fast, gives a good basic understanding of the data.
It finds applications in real-time systems and statistical analysis.
Serves as a baseline model , before using any computationally heavy model and architecture.
Often it is helpful to test certain assumptions like linearity among variables.