Helpful examples of using Support Vector Machine (SVM) machine learning algorithms in scikit-learn.
Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks.
In classification, SVM aims to find the optimal hyperplane that separates data points of different classes with the maximum margin. It uses support vectors, which are the closest data points to the hyperplane, to define the boundary.
For non-linearly separable data, SVM employs kernel functions to transform data into higher dimensions where a linear separator is possible.
In regression, SVM, known as Support Vector Regression (SVR), seeks to fit a model within a specified margin of tolerance, aiming to predict continuous values with minimal deviation.
Both SVM for classification and SVR rely on optimization techniques to minimize error while maximizing the margin or fit within the margin.