The correct answer is: TRUE.
A hard margin kernelized SVM (Gaussian RBF Kernel) is a type of support vector machine (SVM) that uses a Gaussian radial basis function (RBF) kernel to map data points into a high-dimensional space where they can be separated by a hyperplane. This type of SVM is often used for classification problems, but it can also be used for regression problems.
One of the advantages of using a hard margin kernelized SVM is that it is relatively robust to overfitting. This is because the SVM algorithm will only select support vectors that are on the margin of the decision boundary. This means that the SVM will not be overly influenced by noise in the data.
However, there are some cases where a hard margin kernelized SVM can lead to overfitting. This can happen if the data is not well-separated or if the kernel function is not chosen carefully. In these cases, it may be better to use a different type of SVM, such as a soft margin SVM.
Here is a brief explanation of each option:
- Option A: Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting. This is the correct answer.
- Option B: Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) will never lead to overfitting. This is the incorrect answer. As explained above, there are some cases where a hard margin kernelized SVM can lead to overfitting.
I hope this helps!