Hi, all, I was wondering if it somehow possible to define a loss function to the Naive Bayes classifier in scikit-learn. For example, let's assume that we are interested in spam vs. ham classification. In this context, such a loss function would be useful to lower the False Positive rate (i.e., classifying ham as spam, which is "worse" than classifying spam as ham)
For simplicity, I have an example using random data from Gaussian (http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/stat_pattern_class/supervised/parametric/5_stat_superv_parametric.ipynb) Best, Sebastian ------------------------------------------------------------------------------ Slashdot TV. Video for Nerds. Stuff that matters. http://tv.slashdot.org/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
