28th December 2022
I’m looking at AI/ML-powered coding assistants (such as mutable.ai, github’s CoPilot, tabnine, and even Alibaba AI assistant – but there everything was in Chinese so I didn’t proceed at all with it), and found – with sadness – that Kite, one of the longer-existing solutions (since 2014!) has gone out of business…
Here is Kite’s farewell for you to read.
Kite did open-source many parts of their technology/software stack, though I didn’t check how comprehensive those parts are, and if that is anywhere near enough to fork/continue their work.
I wonder if there already exists an open-source project focusing on ML-based code completion for e.g. Python – let me know in the comments if you know one!
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29th May 2012
Usually I’m using 10-fold (non-stratified) CV to measure the predictive power of the models: it gives consistent results, and is easy to perform (at least on smaller datasets).
Just came across the Akaike’s InforÂmaÂtion Criterion (AIC) and Schwarz Bayesian InforÂmaÂtion Criterion (BIC). Citing robjhyndman,
AsympÂtotÂiÂcally, minÂiÂmizÂing the AIC is equivÂaÂlent to minÂiÂmizÂing the CV value. This is true for any model (Stone 1977), not just linÂear modÂels. It is this propÂerty that makes the AIC so useÂful in model selecÂtion when the purÂpose is prediction.
…
Because of the heavÂier penalty, the model choÂsen by BIC is either the same as that choÂsen by AIC, or one with fewer terms. AsympÂtotÂiÂcally, for linÂear modÂels minÂiÂmizÂing BIC is equivÂaÂlent to leave–v–out cross-​​validation when v = n[1-1/(log(n)-1)] (Shao 1997).
Want to try AIC and maybe BIC on my models. Conveniently, both functions exist in R.
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