The most powerful kernel trick of SVM

 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)