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... ------------------------------------------------------------------------------ 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
