SVMs can handle both linear and nonlinear data. In catalysis, this is particularly useful because the relationships between variables (such as catalyst composition, temperature, pressure, and reaction rate) are often nonlinear. By using a kernel trick, SVMs can transform the data into a higher-dimensional space where a linear separator can be found, thus enabling the capture of complex relationships.
