2012/5/11 Mathieu Blondel <[email protected]>: > Another idea is to learn a different classifier on each subset and use a > mixture of the classifiers. As a mixture weight, a simple choice is 1 / > n_mixtures. > > clf = LinearSVC() > clf.fit(X_subset1, y_subset1) > clf2 = LinearSVC() > clf2.fit(X_subset2, y_subset2) > clf.coef_ += clf.coef_ > ... > clf.coef_ /= n_mixtures
Shouldn't you set the intercept_ as well? -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
