<josef.p...@gmail.com> wrote: > (I just went through some articles to see how we can produce p-values > after feature selection with penalized least squares or maximum > penalized likelihood. :)
If you have used penalized least squares or penalized likelihood, you have already pruned the model for parameters that only contributes to overfitting. If the p-value subsequently informs you that a parameter in the model of maximum parsimony is "not significant", you have reached the absurd conclusion that a parameter which should be in the model has no effect. Sturla ------------------------------------------------------------------------------ BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT Develop your own process in accordance with the BPMN 2 standard Learn Process modeling best practices with Bonita BPM through live exercises http://www.bonitasoft.com/be-part-of-it/events/bpm-camp-virtual- event?utm_ source=Sourceforge_BPM_Camp_5_6_15&utm_medium=email&utm_campaign=VA_SF _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
