What are support vector machines?

Question

Accepted Answer

Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks.

SVM works by finding the hyperplane that best separates different classes in the feature space.
It uses support vectors, which are data points closest to the hyperplane, to define the decision boundary.
SVM can handle both linear and non-linear classification using kernel functions (e.g., polynomial, RBF).
Example: In image classification, SVM can distinguish between cats and dogs based on pixel features.
SVM is effective in high-dimensional spaces and is robust against overfitting, especially in high-dimensional datasets.

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