Roberto, I am not sure if this causes problems regarding the implementation, but in any case, I'd recommend you to use the LabelEncoder to have your classes mapped to a fixed range, e.g., 0, 1, 2, 3, 4, 5. And having different class labels in training and test set that reference to the same class is not good practice and could cause all kinds of problems. I just wouldn't risk it even it it works.
> On Apr 30, 2015, at 11:02 PM, Pagliari, Roberto <rpagli...@appcomsci.com> > wrote: > > Suppose I train a classifier with dataset1, which contains labels > > 0 > 3 > 4 > 6 > 7 > > and then predict over dataset2 with labels > > 0 > 3 > 4 > 8 > 10 > > will the hashing be the same for labels 0, 3 and 4? and will scikit learn get > confused by seeing new labels such as 8 and 10? > > Thank you, > > ------------------------------------------------------------------------------ > One dashboard for servers and applications across Physical-Virtual-Cloud > Widest out-of-the-box monitoring support with 50+ applications > Performance metrics, stats and reports that give you Actionable Insights > Deep dive visibility with transaction tracing using APM Insight. > http://ad.doubleclick.net/ddm/clk/290420510;117567292;y_______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ One dashboard for servers and applications across Physical-Virtual-Cloud Widest out-of-the-box monitoring support with 50+ applications Performance metrics, stats and reports that give you Actionable Insights Deep dive visibility with transaction tracing using APM Insight. http://ad.doubleclick.net/ddm/clk/290420510;117567292;y _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
