Improve Linear Regression Using Statistics

As a fresher in the field of machine learning, the first thing that you learn would be simple univariate linear regression. However, for the past decade or so, tree-based algorithms and neural networks have overshadowed the significance of linear regression on a commercial scale. The purpose of this blog post is to highlight why linear regression and other linear algorithms are still very relevant and how you can improve the performance of such rudimentary models to compete with large and sophisticated algorithms like XGBoost and Random Forests. Many self-taught data scientists start code first by learning how to implement various machine learning algorithms without actually understanding the mathematics behind these algorithms. By understanding the math behind these algorithms, we can get an idea about how to improve their performance.