Feature scaling is a crucial step in our preprocessing pipeline that can easily be forgotten. Decision trees and random forests are two of the very few machine learning algorithms where we don't need to worry about feature scaling. Those algorithms are scale invariant. However, the majority of machine learning and optimization algorithms behave much better if features are on the same scale, as we have seen in Chapter 2, Training Simple Machine Learning Algorithms for Classification, when we implemented the gradient descent optimization algorithm.
