Hi all, I came across the following blog post about Andrew Ng's ML class and I like the training / validation errors plots to find out whether the model is too biased (underfitting) or two lax (high variance, over-fitting).
http://digitheadslabnotebook.blogspot.com/2011/12/practical-advice-for-applying-machine.html Has someone tried to apply this trick in practice? Do you think it would be interesting to provide new utilities to easily draw such plots as a way to qualitatively check / validate model selection achieved by automated methods such as grid search CV? Any other comment? -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ Write once. Port to many. Get the SDK and tools to simplify cross-platform app development. Create new or port existing apps to sell to consumers worldwide. Explore the Intel AppUpSM program developer opportunity. appdeveloper.intel.com/join http://p.sf.net/sfu/intel-appdev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
