from sklearn.svm import SVC # "Support vector classifier"
classifier = SVC(kernel='linear', random_state=0)
classifier.fit(x_train, y_train)
#Predicting the test set result
y_pred= classifier.predict(x_test)

{"tags":[{"tag":"textarea","content":"- Not all datapoints are linearly separable\n- Transform such dataset to a higher dimensional space where it can be linearly separable by a hyperplane\n- Finds the maximum margin between classes by finding the largest separation \n- The best separable line is the hyperplane has the biggest margin\n- Support vectors: examples closest to the hyperplane\n- Boundary decision lines : The lines that touches the support vectors \/ closest the the support vectors\n- Soft margin SVM : Used when the classes are not separable (Controlled by regularization parameter)\n- kernel : Sometimes it is difficult to caculate the mapping of transformation. So we use a shortcut called kernel.\n - RBF : support vector = Difference between 2 inputs: X and X` ","code_language":"whatever"}]}