1. Curse of dimensionality: as the dimension of space gets bigger, required size of data (number of distinct features) gets higher -> the model can be overfitted Use dimension reduction methods => select only important orientation, vector that shows high variance of datapoints-> eliminate unimportant features (noise)Ex) PCA, LDA 2. Principal components analysisEx) input data, X [100,4] -> standa..