Data Science, Machine Learning, Algorithms, Support Vector Machine, Supervised Learning.
variables control the bias-variance tradeoff. Having some room for misclassification makes SMVs more flexibility, but it only applies to a limited set of problems. In most real world problems, it’s hard to separate data into two classes with a linear decision boundary. Even with some room for