SVR is a machine learning technique that is generally utilized for continuous or numerical prediction applications. While standard regression models seek to minimize the differences between predicted and actual values, SVR takes a different approach, focused on fitting a “tube” or a hyperplane around the data points. The goal is to fit as many data points as possible inside this tube while keeping variances to a minimum. SVR is very beneficial when dealing with non-linear correlations in data since it uses kernel functions to translate the input features into a higher-dimensional space, allowing complex patterns to be discovered.
One of SVR’s primary advantages is its ability to successfully handle outliers. The algorithm emphasizes points within the tube while ignoring those outside of it. This makes SVR resistant to noise in the data and ensures that extreme values have less influence on the model. SVR has applications in a variety of fields, including finance, biology, and environmental research, where precise prediction of continuous variables is critical. Support Vector Regression is a key tool in the data scientist’s toolset for regression problems due to its flexibility, particularly in detecting subtle patterns and handling outliers.