How does SVM behave when features are not linearly separable in original space?

SVM uses kernel functions to implicitly map data into higher-dimensional space.

When data is not separable in original feature space, kernels transform it into a higher-dimensional space where a linear hyperplane can separate classes. This avoids explicit computation of transformation while preserving computational efficiency.