The main benefit of using Support Vector Regression is that it can deal with the high dimensional data, which is difficult for other algorithms to handle. This algorithm also has less computational cost than other machine learning algorithms.
Support Vector Regression was originally developed for solving classification problems but it can also be used for predicting continuous variables as well as binary variables.
Support Vector Regression (SVR) is a machine learning algorithm that helps identify patterns in data. SVR is a supervised learning algorithm, meaning it requires labeled training data.