I'm trying to get familiar with PCA in relation to other classification methods. I know that if using PCA for preprocessing, then the input data to the machine learning algorithm will rotate. Does this rotation of input affect the classification methods such as KNN, SVM and Random Forest?
Or more precisely are KNN, SVM or Random Forest affected by the transformation in the sense that their classication performance may change if trained and tested on the transformed data compared to the original data before using PCA?
Examples of so is more than welcome, so I hopefully can understand it better.