support-vector-regression

Support vector regression (SVR) applies the same principles as support vector machines but is designed for regression tasks. It aims to find a function that approximates the target data within a specified margin of tolerance. A misconception is that SVR is less powerful than other regression methods; in fact, it performs well in high-dimensional spaces and can model non-linear relationships through kernel functions.