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SUPPORT VECTOR MACHINE

                 SUPPORT VECTOR MACHINE:-

Support vector machine:-it is a type of supervised learning algorithm it is used to solve both classification and regression problem.

Note:- It is mostly used for classification problems.

what we are going to learn in SVM:

a) Support vectors

b) Hyperplane

c) Marginal Distance

d) Linear Separable

e) Non-linear separable

f) support kernels


NOw we will discuss everything in detail.


Hyper plane:-


in the above diagram, we have drawn three lines(A, B, C) separating two data points(stars and reds) The lines (A, B, C) are called Hyperplanes.

Note:-“Select the hyper-plane which segregates the two classes better”

i.e above there are three hyperplanes how to select the best hyperplane?

b)Marginal Distance:-

When we draw a hyperplane the plane creates two new(------) dotted lines one line above the hyperplane and one line below the hyperplane line. see the below image you will get an idea


Suppose c line is my hyperplane and B and A are my marginal lines.
 Now, what is marginal distance:

 we have to measure The maximum distance between the stars and red, The distance is called Marginal distance
Note:-The distance is should always Maximum.

 Support vectors:
what are support vectors 

support vectors are nothing but the nearest data point to the marginal line is called a support vector.
if see the above image there is a small line from the Star symbol to the line B that the data point is called the support vector.  

Note:-The hyperplane is should have a maximum marginal distance near to the data points.


Linear Separable:
linear separable means how the marginal line separating the data points

see below image
how the line is linearly separating the data points this is called linear separable. 




 If you see the below image there is a star in red that data point is called an outlier.
SVM is robust to outliers.
if you are working with SVM to treat outliers.



 Non-linear separable:
I tried to draw the best diagram adjust😂😁
Non-linear separable means see the below image there are red stars and black stars and we create a hyperplane it is separating the data points.
Question: How to solve this issue?
Answer:-when we draw a hyperplane between data points it won't separate data points cleary,
So what will happen means some of the data points are on one side and some of the data are on another side so it will affect the accuracy.
How to solve the above problem.


if you see the above image we have created a small hyperplane
 with the help of support kernels, we are able to do that,
it converts our low dimension data into the high dimension.  


Support kernel:
Support kernel is nothing but converting low dimension data into the high dimension to separate the data points this is called a SUPPORT KERNEL.


for code, part check my GitHub.

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