The kernel trick is most important and powerful technique of SVM . Linear VS Non-Linear dataset linear vs non-linear figure Problem Statement Currently we have learn how to apply SVM algorithm at linear datasets, but what if we have non linear dataset. Solution of Problem Solution is kernel trick. Kernel Trick The Kernel trick is trick where we add many SVMS models by bagging,voting,stacking and boosting or we can use SVM class to implement it. Implementation To implement it follow code given below- from sklearn.svm import SVC svc=SVC() svc.fit(X_train,y_train) svc.score(X_test,y_test)
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