Support Vector Machines

SVM

Support Vector Machines is a supervised machine learning approach which will help in solving both classification and regression problems. 




In the below figure, we have two class of data (red squares and blue circles). The center line is called as hyperplane and helps in classifying the two classes. The hyperplane will create two margin lines with a distance. These margin lines are the parallel line which will pass through the nearest point(s)(support vectors) of each class(line can pass through any number of points if the nearest points are equidistant from the hyperbola).



There can be any kind of hyperplane(vertical/horizontal) but what depends on the hyperplane selection is the marginal distance that is formed. Here the dotted lines as mentioned are called margin lines and the distance between them is called marginal distance(more the distance better the model is). So in this case, we can say that classes are easily separable using the hyperbola that is they are linearly separable.

In the below figure, we can see that the first figure is non linear separable, that is, it is not possible to separate the classes using a hyperplane (even if we try, the accuracy will be very low, less than 50%). 





To solve this type of problem we need SVM kernels. Kernels tries to convert low dimension to high dimension just like the second figure above. In this case, we can easily create a hyperplane between the red and green class. 


Reference:
  1. YouTube: Krish Naik

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