The accuracy (mean correct classification) score is called zero_one_score in scikit-learn:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/metrics.py#L651 If your data is imbalanced then the accuracy will likely to be artificially high. For instance if you have 95% of negative examples and 5% of positive example, a dummy model always predicting "-1" will get 95% accuracy. That's why it's better to use f1_score or the Area under ROC for such cases. ------------------------------------------------------------------------------ RSA(R) Conference 2012 Save $700 by Nov 18 Register now http://p.sf.net/sfu/rsa-sfdev2dev1 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
