sklearn.svm.svc
时间: 2023-12-23 21:03:49 浏览: 25
The SVC (Support Vector Classification) model in scikit-learn is a type of SVM (Support Vector Machine) algorithm used for classification tasks. SVC seeks to find the best hyperplane that separates the data into different classes by maximizing the margin between the hyperplane and the closest data points (support vectors). It can handle both linear and non-linear classification problems by using different types of kernel functions such as linear, polynomial, and radial basis function (RBF). The model can be trained using the fit() method and can make predictions on new data using the predict() method. It also provides various parameters for tuning the model, such as C (regularization parameter), kernel type, and gamma (kernel coefficient).