2012/9/12 Christian Jauvin <[email protected]>: > Hi, > > As I only have an intuitive notion of how "sample_weight" (i.e. to be > fed to certain types of classifier) should work, I'd like to know if > this is a sound way of computing them: > > def get_sample_weight(y): > p = 1. / len(np.unique(y)) > bc = np.bincount(y) > w = np.repeat(p, len(y)) > for i, v in enumerate(y): > w[i] /= bc[v] > assert np.sum(w) == 1 > return w
Sample weight are to be used when you have application-specific, per-sample cost to take into account (e.g. the money you would loose by misclassifying a specific sample). If you just want to give a cost to account for class-imbalance, you should use class_weight='auto' (or a class_weight on your own with your own specific cost renormalization on a per-class basis). -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ 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
