I am new in the ML. I know that overfitting is memorizing the data while training. Like in Neural Network, if we make lots of layers and lots of hidden nodes, we can memorize all the data, but it can be bad because train data would not cover the whole space.
Like this, is there any way to overfit in SVM and Logistic Regression? Since they are linear algorithms, they cannot be something curvy, I guess, so I am guessing the answer would be no. But I am not sure.
Any help is appreciated.