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