Why Feature Scaling Is Required in Many Algorithms

Algorithms like kNN, SVMs, and gradient descent assume that features share comparable scales.

If one feature ranges from 0 to 1 and another from 0 to 10000, the larger one dominates distances and gradients.

Scaling equalizes the influence so the model focuses on relative patterns.

Key takeaway❔

Unscaled data hides structure. Scaling makes patterns visible for the algorithm.