Support Vector Machines (SVM): An Intuitive Explanation
<p>Support Vector Machines (SVMs) are a type of supervised machine learning algorithm used for classification and regression tasks. They are widely used in various fields, including pattern recognition, image analysis, and natural language processing.</p>
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<p>SVMs work by finding the optimal hyperplane that separates data points into different classes.</p>
<h2>Hyperplane:</h2>
<p>A <strong>hyperplane </strong>is a decision boundary that separates data points into different classes in a high-dimensional space. In two-dimensional space, a hyperplane is simply a line that separates the data points into two classes. In three-dimensional space, a hyperplane is a plane that separates the data points into two classes. Similarly, in <strong>N-dimensional space</strong>, a hyperplane has <strong>(N-1)-dimensions</strong>.</p>
<p>It can be used to make predictions on new data points by evaluating which side of the hyperplane they fall on. Data points on one side of the hyperplane are classified as belonging to one class, while data points on the other side of the hyperplane are classified as belonging to another class.</p>
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