Introduction to SVM

Gain an understanding of SVM, and the concepts of signed and unsigned distance.

Support vector machine (SVM) is a popular and powerful supervised learning algorithm for classification and regression problems. It works by finding the best possible boundary between different classes of data points. In this lesson, we’ll cover the basic concepts and principles behind SVMs and see how they can be applied in practice.

What is SVM?

Suppose a person works for a bank, and their job is to decide whether to approve or reject loan applications based on the applicant’s financial history. They have a loan dataset with various features such as credit score, income, and debt-to-income ratio, along with past approval and rejection records. The task is to use SVM to build a predictive model for future loan applications.

First, they map each loan application into a feature space based on its features and label each loan application as either “approved” or “rejected,” which creates two different classes in the dataset. Next, they try to find a decision boundary that will separate the data linearly.

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