Well that explains why I was struggling to find anything online!
Thanks for the link, I’ve been going through some of the techniques.
Using AIC the penalty for each additional parameter is a factor of e. For BIC the equivalent is √n so the more samples the more penalised a complex model is. For large n the models diverge—are there principled methods for choosing which regularisation to use?
At this point I reveal that I just play a statistician on the net. I don’t know how people choose from among the many methods available. Is there a statistician in the house?
Well that explains why I was struggling to find anything online!
Thanks for the link, I’ve been going through some of the techniques.
Using AIC the penalty for each additional parameter is a factor of e. For BIC the equivalent is √n so the more samples the more penalised a complex model is. For large n the models diverge—are there principled methods for choosing which regularisation to use?
At this point I reveal that I just play a statistician on the net. I don’t know how people choose from among the many methods available. Is there a statistician in the house?