2011/9/25 <[email protected]>: > The predict_proba are just nonlinear monotonic transformations of the > parameters. So the difference is only in specifying the convergence > tolerance.
That's what I thought, and I'd be so lazy to let the client determine the tolerance parameter ;) > However, the problem that we just had is the complete (quasi-) separation > case. In this case the predict_proba converge to 0 and 1, while the > parameters will go off to infinity. > So the boundary behavior might be messy. Right, so unless I map the parameters back from log-space to [0,1] (which is exactly what NB's predict_proba does), predict_proba would actually be a safer bet than coef_ + intercept_? -- Lars Buitinck Scientific programmer, ILPS University of Amsterdam ------------------------------------------------------------------------------ All of the data generated in your IT infrastructure is seriously valuable. Why? It contains a definitive record of application performance, security threats, fraudulent activity, and more. Splunk takes this data and makes sense of it. IT sense. And common sense. http://p.sf.net/sfu/splunk-d2dcopy2 _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
