Support Vector Machines

SVM (support vector machine) is one of the variant of  supervised learning algorithm.It is considered as one of the best classification algorithm.It works well with the binary classifier but with little modification  can also work with the multiple classes as well.The complete idea of Support Vectors resides on finding the best hyperplane that classifies our data set.



What is HyperPlane ?

The line which divided the data set and classifies them.In the above example , the line(decision Boundary) classifies the data set into two different classes.
It should be noted that farther the datapoint from the Hyperplane the more correctly it has been classified.

In case of two-dimensional architecture , it can be easy to find a plane that classifies our data but in real time ,we have to face the multi-dimensional data and there is no clear classification then  it is inefficient to use the above mentioned concept.In such scenario, the concept of kernel comes into the picture which we will dicuss in our coming tutorials.

What are Support Vectors ?

Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane.

Advantages of SVM :-

1.) Works well with smaller data set.
2.) Have a good accuracy

Disadvantages of SVM :-

1.) Not so efficient with enormous data set
2.) Noisier data set is difficult to handle.

We will learn more about SVM kernel and it's python implementation in our upcoming tutorials.