Motivation

Linear regression models are the building blocks of many learning algorithms, but this is not the only reason behind their popularity. The following are the key factors behind their popularity:

• Widely used: Linear regression is the oldest regression technique and it's widely used in many applications, such as forecasting and financial analysis.
• Runs fast: Linear regression algorithms are very simple and don't include mathematical computations which are too expensive.
• Easy to use (not a lot of tuning required): Linear regression is very easy to use, and mostly it's the first learning method to learn about in the machine learning or data science class as you don't have too many hyperparameters to tune in order to get better performance.
• Highly interpretable: Because of its simplicity and ease of inspecting the contribution of each predictor-coefficient pair, linear regression is highly interpretable; you can easily understand the model behavior and interpret the model output for non-technical guys. If a coefficient is zero, the associated predictor variable contributes nothing. If a coefficient is not zero, the contribution due to the specific predictor variable can easily be ascertained.
• Basis for many other methods: Linear regression is considered the underlying foundation for many learning methods, such as neural networks and its growing part, deep learning.