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    Kite AI coding assistant is saying farewell

    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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    Posted in Machine learning, Programming, Software, Technologies | No Comments »

    Information criteria for choosing best predictive models

    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 Infor­ma­tion Criterion (AIC) and Schwarz Bayesian Infor­ma­tion Criterion (BIC). Citing robjhyndman,

    Asymp­tot­i­cally, min­i­miz­ing the AIC is equiv­a­lent to min­i­miz­ing the CV value. This is true for any model (Stone 1977), not just lin­ear mod­els. It is this prop­erty that makes the AIC so use­ful in model selec­tion when the pur­pose is prediction.

    Because of the heav­ier penalty, the model cho­sen by BIC is either the same as that cho­sen by AIC, or one with fewer terms. Asymp­tot­i­cally, for lin­ear mod­els min­i­miz­ing BIC is equiv­a­lent 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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