Hi, Thank you for the awesome article. I have a question. Say there is a data set of n examples and p variables (X = nxp). The goal is to classify the data examples in to C classes. Let's say we conduct a specific pre-processing step to the data set which does not change the dimensions (X_preprocessed = nxp). Classifying the pre-processed data (X_preprocessed) is easier (classification accuracy is high) than the non-processed one (X). When I checked the number of principal components in X according to the Kaiser-Guttman criterion, it is much higher than the number of principal components in X_preprocessed. Does having less number of principal components signal that the particular data set can be classified into classes much easily? Basically what does it mean when the number of principal components are lower?