Does SVM Need Feature Scaling Or Normalization?
In Support Vector Machines (SVM), feature scaling or normalization are not strictly required, but are highly recommended, as it can significantly improve model performance and convergence speed. SVM tries to find the optimal hyperplane that separates the data points of different classes with the maximum margin. If the features are on different scales, the hyperplane will be heavily influenced by the features with larger values, potentially leading to suboptimal results....