scikits

I am trying to train an SVM in scikit. I am following the example and tried to adjust it to my 3d feature vectors. I tried the example from the page http://scikit-learn.org/stable/modules/svm.html and it ran through. While bugfixing I came back to the tutorial setup and found this: X = [[0, 0], [1, 1],[2,2]] y = [0, 1,1] clf = svm.SVC() clf.fit(X, y) works while X = [[0, 0,0], [1, 1,1],[2,2,2]] y = [0, 1,1] clf = svm.SVC() clf.fit(X, y) fails with: ValueError: X.shape[1] = 2 should be equal to 3, the number of features at training time what is wrong here? It's only one additional dimension...

I am trying to compute area under the ROC curve using sklearn.metrics.roc_auc_score using the following method: roc_auc = sklearn.metrics.roc_auc_score(actual, predicted) where actual is a binary vector with ground truth classification labels and predicted is a binary vector with classification labels that my classifier has predicted. However, the value of roc_auc that I am getting is EXACTLY similar to accuracy values (proportion of samples whose labels are correctly predicted). This is not a one-off thing. I try my classifier on various values of the parameters and every time I get the same

First of all I started with python yesterday. I'm trying to do text classification with SciKit and a large dataset (250.000 tweets). For the algorithm, every tweet will be represented as a 4000 x 1 vector, so this means the input is 250.000 rows and 4000 columns. When i try to construct this in python, I run out of memory after 8500 tweets (when working with a list and appending it) and when I preallocate the memory I just get the error: MemoryError (np.zeros(4000,2500000)). Is SciKit not able to work with these large datasets \? Am I doing something wrong (as it is my second day with python)?