SVM — Support Vector Machine

# Basic idea of SVM

“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

# SVM goal:

Then, we perform classification by finding the hyper-plane that differentiates the two classes very well. We want that the distance of both the class must be far apart from Decision boundary or hyper plane.

# Hyperplane:

A plane your points with maximum margin.

Our data is in 3D space and the separating hyperplane is a 2D image ie n-1 dimensional

# Mathematics in SVM:

# Goal

To maximize the minimum distance of point from the hyper plane

# Reformulation

The point which are near hyperplane are called support vectors. They should always lie on hyperplane

# Pegasos Algorithm for Unconstrained Optimization

It said SVM is unconstrained convex optimization problem

# Coding

# loss

# loss graph

# how Hyperplane looks like



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