How does SVM handle non-linearly separable data?

SVM uses kernel functions to transform data into higher dimensions where it becomes linearly separable.

When data is not linearly separable, SVM applies kernel functions like RBF or polynomial kernels to implicitly map data into higher-dimensional feature spaces. In this space, a linear hyperplane can separate the classes effectively.