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DATA SCIENCE THEORY | MACHINE LEARNING | ALGORITHMS
The Complete Guide to Support Vector Machines: From Theory to Practice
Hyperparameters, advantages, limitations, real-world applications and more
In the vast landscape of machine learning algorithms, Support Vector Machines (SVMs) stand out as a powerful and elegant solution for classification problems. Originally developed in the 1990s, SVMs have proven their worth across numerous fields, from medical diagnosis to text classification. But what makes them so special, and when should you consider using them?
Understanding SVMs Through Everyday Examples
Imagine you’re organising a massive library with thousands of books. Your task is to separate fiction from non-fiction, but it’s not always a clear-cut decision. Some books, like historical fiction or creative non-fiction, blur the lines between categories. This is exactly the kind of challenge that SVMs excel at handling.
An SVM works by finding the best possible dividing line (or plane in higher dimensions), the “hyperplane” between different categories. But it doesn’t just find any line , it finds the line that creates the widest possible “corridor” between the categories. Think of…
