SVM (Support Vector Machines) in scikit-learn Question Title * 1. What is a margin in SVM? A parameter that is learned by an SVM algorithm. None of the variants. A maximum width the decision boundary area can be increased before hitting a data point. A hyperparameter that denotes number of parameters in SVM algorithm. Question Title * 2. What is a kernel in SVM? None of the variants. An optimization algorithm. A similarity measure, modified dot product between data points. A loss function in SVM. Question Title * 3. (single choice) What is the purpose of a C parameter in SVM? It defines the power of regularization. It defines the speed of algorithm’s learning. It defines the smoothness of decision boundary. It defines the architecture of the algorithm. Question Title * 4. (single choice) What is the name of the class imported from sklearn.svm that is used for regression with SVM? SVR. SVM. SVC. SVK. Question Title * 5. (single choice) When we choose a kernel to be polynomial, what is a parameter degree? Algorithm’s learning speed. None of the variants. A degree of polynomial. A number of samples that will be transformed to polynomials. Question Title * 6. (single choice) What functions does a kernel support? Linear. Sigmoid. Polynomial. All of the above. Готово