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 binar...

I have started a grid search for SVM parameters in a rather wide range. The most of the search space have been calculated and now I got one last process, which goes already for 100 hours. I'd li...

Trying to load custom data to perform NB Classification in Scikit. Need help in loading the sample data into Scikit and then perform NB. How to load categorical values for target. Use the same dat...

I need some advice on selecting statistics package for Python, I've done quite some search, but not sure if I get everything right, specifically on the differences between statsmodels and sci...

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...

I am training linear regression model using a data-set which has real valued labels in the interval [0,10]. My predicted values on the test set have some predictions exceeding 10. Is there a way to...

I am training a svm classifier with cross validation (stratifiedKfold) using the scikits interfaces. For each test set (of k), I get a classification result. I want to have a confusion matrix with ...

I have trained a bunch of RBF SVMs using scikits.learn in Python and then Pickled the results. These are for image processing tasks and one thing I want to do for testing is run each classifier on ...

Is class sklearn.cross_validation.ShuffleSplit( n, n_iterations=10, test_fraction=0.10000000000000001, indices=True, random_state=None ) the right way for 10*10fold CV in scikit-learn?...

I'd like to fit a scikits.learn.hmm.GaussianHMM to training sequences of different length. The fit method, however, prevents using sequences of different length by doing obs = np.asanyarray(obs) ...

In my scikits-learn Pipeline, I would like to pass a custom vocabulary to CountVectorizer(): text_classifier = Pipeline([ ('count', CountVectorizer(vocabulary=myvocab)), ('tfidf', TfidfTransform...

I have two different timeseries with partially overlapping timestamps: import scikits.timeseries as ts from datetime import datetime a = ts.time_series([1,2,3], dates=[datetime(2010,10,20), datet...