2014-10-20 22:08 GMT+02:00 George Bezerra <gbeze...@gmail.com>: > Not an expert, but I think the idea is that you remove (or add) features one > by one, starting from the ones that have the least (or most) impact. > > E.g., try removing a feature, if performance improves, keep it that way and > move on to the next feature. It's a greedy approach; not optimal, but avoids > exponential complexity.
No. That would be backward stepwise selection. Neither that, nor its forward cousin (find most discriminative feature, then second-most, etc.) are implemented in scikit-learn. The feature selection in sklearn.feature_selection computes a score per feature (in practice always a significance test, but the API is set up so that other scores are possible), then keeps the k best features, the p% best, or the ones that don't exceed some threshold/p-value. ------------------------------------------------------------------------------ Comprehensive Server Monitoring with Site24x7. Monitor 10 servers for $9/Month. Get alerted through email, SMS, voice calls or mobile push notifications. Take corrective actions from your mobile device. http://p.sf.net/sfu/Zoho _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
