2012/6/22 Kai Kuehne <[email protected]>: > I didn't explain the thing I don't understand. > I try again... > > In this first picture: > http://jakevdp.github.com/_images/plot_bias_variance_examples_3.png > > Both training and cross validation error start high, so it's a high bias > if the degree is small. > > On the second picture: > http://jakevdp.github.com/_images/plot_bias_variance_examples_4.png > > On the left side pictured is d = 1, so a low degree. > The cross validation error starts high, but ... and that's the thing > I both don't understand and cannot reproduce: The training error > starts small. The first diagram states that both start high for small > degrees... >
I don't really know but I think those curves should be recomputed to display the mean across 10 runs of a 10-folds CV along with the standard error of the means as error bars like I almost did on my graphs (I used the standard deviation instead). -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ 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
