Machine Learning, Svm, Support Vector Machine, Data Science, Kernel.
systematically handle perfectly/almost linearly separable data. How does it handle the cases where the data is absolutely not linearly separable? Afterall, a lot of real-world data falls in this category. Surely, finding a hyperplane can’t work anymore. This seems unfortunate given that SVMs excel