Regression models are very popular in machine learning and are widely applied in many areas. Linear regression's main advantage is the simplicity of representing the dataset as a simple linear model. Hence, the training time for linear regression is fast. Similarly, the model can be inspected by data scientists to understand which variable is contributing to the decisions of the overall model. Linear regression is recommended in cases where the problem statement is simple and few variables are used for predictions. As the complexity of the dataset increases, linear regression may generate significant errors if the data has a lot of noise in it.
Linear regression makes a bold assumption that the dependent variable has a linear relationship with the regressors. If this does not hold true, then the linear regression algorithm may not be able to...
