Unlocking the Power of Support Vector Machines: A Comprehensive Guide to SVMs
Basic Terminologies and Applications
Intro
Support Vector Machines (SVMs) are one of the most popular and widely used algorithms in the field of machine learning. They are particularly well-suited for classification problems, where the goal is to predict the class label of a new instance based on its features.
In this blog post, we will explore the use cases of SVMs in Big Tech, the pros and cons of this model, how it works, what to optimize, what to look for, and the best open-source packages that can help you solve problems using SVMs.
How SVM’s Work?
SVM is a type of algorithm that helps us draw a line or boundary between different groups of data points. Imagine you have a set of dots on a piece of paper, and some of those dots are red and some are blue. Your task is to draw a line that separates the red dots from the blue dots.
SVM helps us find the best possible line that separates the two groups with the largest possible margin. This means that the distance between the line and the closest dots from both groups is as big as possible. The line is called a “hyperplane,” which is a fancy way of saying it’s a higher-dimensional version of a regular line.
