👉 Supports computing, also known as support vector machine (SVM), is a powerful machine learning algorithm used for classification and regression tasks. It works by finding the hyperplane that best separates data points of different classes in a high-dimensional space, maximizing the margin between them. This margin is crucial for improving the model's generalization ability to unseen data. SVMs are particularly effective when dealing with high-dimensional spaces and can handle both linear and non-linear data by employing kernel functions, which transform the input space into higher dimensions where a clear separation is possible. Despite its effectiveness, SVMs can be computationally intensive and require careful parameter tuning to achieve optimal performance.
