Advantages and Disadvantages of Linear Regression

Linear regression is a simple Supervised Learning algorithm that is used to predict the value of a dependent variable(y) for a given value of the independent variable(x) by effectively modelling a linear relationship(of the form: y mx c) between the input(x) and output(y) variables using the given dataset. In this article we will be discussing the advantages and disadvantages of linear regression. Linear Regression is a very simple algorithm that can be implemented very easily to give satisfactory results.Furthermore, these models can be trained easily and efficiently even on systems with relatively low computational power when compared to other complex algorithms.Linear regression has a considerably lower time complexity when compared to some of the other machine learning algorithms.The mathematical equations of Linear regression are also fairly easy to understand and interpret.Hence Linear regression is very easy to master. Linear regression fits linearly seperable datasets almost perfectly and is often used to find the nature of the relationship between variables. Overfitting is a situation that arises when a machine learning model fits a dataset very closely and hence captures the noisy data as well.This negatively impacts the performance of model and reduces its accuracy on the test set.