C-Support Vector Classification

C-Support Vector Classification (C-SVC) is a machine learning method used to categorize data into different groups. It works by finding the best boundary that separates the groups with the widest possible margin. The parameter “C” balances two goals: achieving a clear separation and allowing some misclassifications to improve overall accuracy. A small C allows more errors for a broader margin, while a large C prioritizes fitting the training data closely. This approach helps to build a model that accurately classifies new, unseen data based on the patterns learned from the training set.