Dear MLLab, I am not quite sure what task 3.3c is asking. This is my understanding of how we build the classifier: for the training phase, we apply PCA first to project the given data, and in our case this means the 2D and 1D PCA to prepare the data to be classified as red/not red and green/not green respectively. Then we train to SVMs for red/not red and green/not green. For the actual classifier itself, the data is projected with with the red PCA matrix and the green PCA matrix. These processed data are given as input to the red and green SVMs respectively (in a sequential manner). Then the result is constructed in the obvious way based on the SVM results. However, I am particularly confused by the comment “print accuracy on 1, 2, 4 dimensions”, for based on my understanding, we are supposed to use at least two different dimensions in our classifier. Jonathan