SVM is a strong supervised machine learning method that is used for classification and regression tasks. SVM’s basic idea is to find a hyperplane in a high-dimensional space that best separates data points of distinct classes. The “support vectors” are the data points nearest to the decision boundary or hyperplane. The margin, which is the distance between the support vectors and the decision boundary, is maximized by SVM. A wider margin indicates better generalization to previously unseen data and increased resistance to noise in the training data.
SVM’s capacity to handle non-linear correlations in data using kernel functions is one of its primary strengths. Kernel functions convert the input features into a higher-dimensional space, allowing a hyperplane to be found that efficiently separates the data in this transformed space. As a result, SVM can capture complicated decision boundaries and achieve high accuracy in a wide range of scenarios. Furthermore, SVM is less prone to overfitting than other algorithms since the margin maximization encourages a more generalizable model.
While SVMs thrive in many applications, they may struggle with huge datasets or several classes. SVM training on a large dataset can be computationally expensive, and the method may suffer if the number of features exceeds the number of samples. Despite these limitations, SVM continues to be a popular choice in a variety of disciplines, including image classification, text categorization, and bioinformatics, due to its versatility and effectiveness in dealing with different and complicated datasets.