Изучите основные концепции, делающие Support Vector Machine мощным линейным классификатором.
1. Support Vectors: These are the data points that lie closest to the decision boundary. They are the most important data points for the model as they define the boundary.
2. Decision Boundary: This is the line that separates the classes in the feature space. It is determined by the support vectors.
3. Margin: This is the distance between the decision boundary and the nearest support vectors. The goal of SVM is to maximize this margin.
4. Soft Margin: In real-world datasets, it's not always possible to separate classes perfectly. Soft margin allows for some misclassifications by introducing slack variables.
5. Kernel Trick: This allows SVM to perform non-linear classification by transforming the original feature
towardsdatascience.com
Introduction to Support Vector Machines — Motivation and Basics
Create attached notes ...