SVMs function by mapping input data into a higher-dimensional space where a linear separator, or hyperplane, can be found. This is achieved using a kernel function, which transforms the input data into a different space. The main goal is to find a hyperplane that maximizes the margin, or distance, between different classes of data points. This capability is crucial for predictive modeling in catalysis, where complex, multidimensional datasets are common.
