Hi again, I looked at Olivers diagrams. I plot the error, Oliver the classification score which starts low (high error). So it's the same problem. Or isn't this even a problem? I'm getting confused.
I try to grasp the concept based on http://jakevdp.github.com/_images/plot_bias_variance_examples_4.png where, in both cases, the training error starts small. But then I looked at this picture: http://jakevdp.github.com/_images/plot_bias_variance_examples_3.png I see that for small degrees, the classifier will always have a high bias (all sets show high error rates). When I use the bayes classifier, what is the degree? Isn't it always 1 which leads to the observed behavior? Thanks! ------------------------------------------------------------------------------ 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
