Some machine learning models, such as decision trees and random forests, don't require feature scaling, which may come as a surprise to many. While accuracy is a straightforward metric for classification models, regression evaluation is more complex. The R squared metric provides a clearer understanding of how well a regression model predicts numerical outcomes, with values closer to one indicating better performance.
