# Why isn't Akaike information criterion used more in machine learning?

I just ran into "Akaike information criterion", and I noticed this large amount of literature on model selection (also things like BIC seem to exist).

Why don't contemporary machine learning methods take advantage of these BIC and AIC model selection criteria?

• because nobody's calculating the likelihoods? – Aksakal Nov 15 '17 at 14:16
• What do you mean by "contemporary machine learning methods"? As far as I used AIC and BIC are used frequently. – Ferdi Nov 15 '17 at 14:16
• It is simply an adjusted error variance taking into account the sample size and the # of parameters. In my view/religion/belief system it is simply descriptive and not inferential. – IrishStat Nov 15 '17 at 14:19
• Also why the -1? Remember there are no stupid questions -- each question tries to shed light on the universe – echo Nov 15 '17 at 14:28
• @echo: I didn't downvote, but I think your question would be improved if you could source/support the main claim (that machine learning methods do take advantage of these BIC and AIC model selection criteria) – user603 Nov 15 '17 at 14:32

Then finally there is the issue of the # of parameters which are required for BIC and AIC. With general function approximators (e.g. KNNs) on real-valued inputs, it is possible to "hide" parameters, i.e. to construct a real number which contains the same information as two real numbers (think e.g. of intersecting the digits). In that case, what is the actual number of parameters? On the other hand, with more complicated models, you may have constraints on your parameters, say you can only fit parameters such that $\theta_1 > \theta_2$ (see e.g. here). Or you may have non-identifiability, in which case multiple values of the parameters actually give the same model. In all these case, simply counting of parameters does not give a suitable estimate.