Does Linear Regression Require Feature Scaling?
In linear regression, feature scaling is not strictly required but can be beneficial in certain situations. When using gradient descent-based optimization algorithms, feature scaling can help speed up convergence and improve model performance. However, when employing a closed-form solution like the normal equation, feature scaling is not necessary, as the algorithm naturally handles features with different scales In this tutorial, we will explore the impact of feature scaling on linear regressions’s performance using the Red Wine dataset as an example....