Why Linear Regression Is Still Relevant in the Age of Deep Learning
In today’s data-driven world, the buzz is often about deep learning models, transformers, and neural networks that power cutting-edge applications like ChatGPT, autonomous vehicles, and advanced medical diagnostics. With so much attention on these complex architectures, it’s easy to assume that simpler statistical methods, like linear regression , have lost their place. But in reality, linear regression continues to play a vital role in data analysis and machine learning, often serving as the foundation upon which more advanced techniques are built. Here’s why linear regression is still highly relevant, even in the age of deep learning. 1. Simplicity and Interpretability One of linear regression’s strongest advantages is its transparency. Unlike deep learning models, which can function as “black boxes,” linear regression provides a clear relationship between independent variables (predictors) and the dependent variable (outcome). For example, a business can use linear regression to und...
