2018-06-05 1:06 GMT+09:00 Andreas Mueller <t3k...@gmail.com>: > Is that Jet?! > > https://www.youtube.com/watch?v=xAoljeRJ3lU > > ;) >
Quite an entertaining presentation and informative to the non-expert about color theory, though I'm not sure I'd go so far as to call jet "evil" and that everyone hates it. Actually, I didn't know that the colormap known as Jet actually had a name...I had reversed engineered it to reproduce what I saw elsewhere. I suppose I'm glad I have already built my infrastructure's version of the metric surface plotter to allow complete color customization at runtime from the CLI, and can then tailor results to my audiences. :) I'll keep this video's explanation in mind - thanks for the reference. Cheers, J.B. > On 6/4/18 11:56 AM, Brown J.B. via scikit-learn wrote: > > Hello community, > > I wonder if there's something similar for the binary class case where, >>> the prediction is a real value (activation) and from this we can also >>> derive >>> - CMs for all prediction cutoff (or set of cutoffs?) >>> - scores over all cutoffs (AUC, AP, ...) >>> >> AUC and AP are by definition over all cut-offs. And CMs for all >> cutoffs doesn't seem a good idea, because that'll be n_samples many >> in the general case. If you want to specify a set of cutoffs, that would >> be pretty easy to do. >> How do you find these cut-offs, though? >> >>> >>> For me, in analyzing (binary class) performance, reporting scores for >>> a single cutoff is less useful than seeing how the many scores (tpr, >>> ppv, mcc, relative risk, chi^2, ...) vary at various false positive >>> rates, or prediction quantiles. >>> >> > In terms of finding cut-offs, one could use the idea of metric surfaces > that I recently proposed > https://onlinelibrary.wiley.com/doi/abs/10.1002/minf.201700127 > and then plot your per-threshold TPR/TNR pairs on the PPV/MCC/etc surfaces > to determine what conditions you are willing to accept against the > background of your prediction problem. > > I use these surfaces (a) to think about the prediction problem before any > attempt at modeling is made, and (b) to deconstruct results such as > "Accuracy=85%" into interpretations in the context of my field and the data > being predicted. > > Hope this contributes a bit of food for thought. > J.B. > > _______________________________________________ > scikit-learn mailing > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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