## not Bayesian enough?!

**O**ur random forest paper was alas rejected last week. Alas because I think the approach is a significant advance in ABC methodology when implemented for model choice, avoiding the delicate selection of summary statistics and the report of shaky posterior probability approximation. Alas also because the referees somewhat missed the point, apparently perceiving random forests as a way to project a large collection of summary statistics on a limited dimensional vector as in the Read Paper of Paul Fearnhead and Dennis Prarngle, while the central point in using random forests is the avoidance of a selection or projection of summary statistics. They also dismissed ou approach based on the argument that the reduction in error rate brought by random forests over LDA or standard (k-nn) ABC is “marginal”, which indicates a degree of misunderstanding of what the classification error stand for in machine learning: the maximum possible gain in supervised learning with a large number of classes cannot be brought arbitrarily close to zero. Last but not least, the referees did not appreciate why we mostly cannot trust posterior probabilities produced by ABC model choice and hence why the posterior error loss is a valuable and almost inevitable machine learning alternative, dismissing the posterior expected loss as being *not Bayesian enough* (or at all), for “averaging over hypothetical datasets” (which is a replicate of Jeffreys‘ famous criticism of p-values)! Certainly a first time for me to be rejected based on this argument!

January 23, 2015 at 8:38 pm

Any thoughts about putting out some software for this? Would love to try it. R? Python?

January 24, 2015 at 8:11 am

We definitely plan to make random forests the tool for model selection in the next version of DIYABC!

January 23, 2015 at 10:41 am

I’m sure that you’ll one day be sufficiently Bayesian… Maybe you should read The Bayesian Choice by some guy I don’t remember. It’s pretty convincing :p