Data Science, Machine Learning, Algorithms, Support Vector Machine, Supervised Learning.
might be a good candidate to separate these classes. So let’s fit an SVM with a second-degree polynomial kernel. from sklearn import svm model = svm.SVC(kernel='poly', degree=2) model.fit(x_train, y_train) To see the result of fitting this model, we can plot the decision boundary and the margin