Further to my last message, there seems to be a rather serious regression in OneVsRestClassifier in git master. I didn't find an issue about it.
The following reproduces the behaviour in question: import numpy as np import sklearn from sklearn.multiclass import OneVsRestClassifier import sklearn.svm as svm rng = np.random.seed(0) train = np.random.randn(1000, 400) train_l = np.random.random_integers(1, 10, size=(1000,)) test = np.random.randn(1000, 400) test_l = np.random.random_integers(1, 10, size=(1000,)) svm = OneVsRestClassifier(svm.SVC(C=0.01, scale_C=False)).fit(train, train_l) print "Training accuracy:", (train_l ≡ svm.predict(train)).mean() print "Test accuracy:", (test_l ≡ svm.predict(test)).mean() print "scikit-learn version:", sklearn.__version__ wardefar@atchoum:~$ python test.py /u/wardefar/.local/lib/python2.7/site-packages/scikit_learn-0.10-py2.7-linux-x86_64.egg/sklearn/svm/classes.py:184: FutureWarning: SVM: scale_C will be True by default in scikit-learn 0.11 cache_size, scale_C) Training accuracy: 1.0 Test accuracy: 0.096 scikit-learn version: 0.10 wardefar@atchoum:~$ python test.py /u/wardefar/.local/lib/python2.7/site-packages/scikit_learn-0.11_git-py2.7-linux-x86_64.egg/sklearn/svm/classes.py:228: FutureWarning: SVM: scale_C will disappear and be assumed to be True in scikit-learn 0.12 cache_size, scale_C, sparse="auto") Training accuracy: 0.0 Test accuracy: 0.107 scikit-learn version: 0.11-git ------------------------------------------------------------------------------ Virtualization & Cloud Management Using Capacity Planning Cloud computing makes use of virtualization - but cloud computing also focuses on allowing computing to be delivered as a service. http://www.accelacomm.com/jaw/sfnl/114/51521223/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
