This would probably be more useful if users can provide their own examples of abnormal and normal expenses. In that case, the model itself is probably not very difficult; I imagine a variety of off the shelf toolkits would work. To me, the harder part seems like making the workflow smooth and robust -- deciding how users would flag outliers, run the classifier, correct misclassifications, cause retraining to happen, etc.
On Wed, Jan 24, 2024 at 10:05 AM Yichu Zhou <flyaway1...@gmail.com> wrote: > There is a kind of machine learning problem called outlier detection. I > think sciki-learn library is a good starting point if we want to use ML > techniques. But in our case, I feel the definition of “abnormal” varies on > different personal situations. It might be tricky to formulate the problem > properly. > > On Tue, Jan 23, 2024 at 21:24 Red S <redstre...@gmail.com> wrote: > >> Definitely! That's what I had in mind. Would you or others on this list >> have experience in how to frame the problem from a deep learning >> classification problem, what tools/libraries to use, and such? Pointers >> appreciated. >> >> On Tuesday, January 23, 2024 at 8:08:31 AM UTC-8 char...@gmail.com wrote: >> >> Sounds like a good opportunity for deep learning classification problem. >> >> On Friday, January 19, 2024 at 11:45:35 AM UTC+1 Red S wrote: >> >> I'm curious, has anyone setup Beancount scripts or reports to flag >> expenses that might need further attention? The situation that made me >> think about this is a quarterly bill that doubled multiple times after >> years of being stable, which is an obvious red flag. >> >> Unlike in the past, virtually of my Beancount interactions are highly >> automated, which combined with the fact that time is at a premium these >> days, causes me to miss details like this. >> >> In this particular case, a rule to flag expenses that deviate from their >> norm over a certain time period (monthly, annually) might be simple to >> write, but I was wondering for a more general, perhaps fancier solution >> that would learn to distinguish what's normal and call attention to what's >> not, as rules based solutions tend to be incomplete and require constant >> fiddling. >> >> -- >> You received this message because you are subscribed to the Google Groups >> "Beancount" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to beancount+unsubscr...@googlegroups.com. >> To view this discussion on the web visit >> https://groups.google.com/d/msgid/beancount/58ec4e60-530f-4055-990b-03e374251a29n%40googlegroups.com >> <https://groups.google.com/d/msgid/beancount/58ec4e60-530f-4055-990b-03e374251a29n%40googlegroups.com?utm_medium=email&utm_source=footer> >> . >> > -- > You received this message because you are subscribed to the Google Groups > "Beancount" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to beancount+unsubscr...@googlegroups.com. > To view this discussion on the web visit > https://groups.google.com/d/msgid/beancount/CACDkcs-w4K8p3a2v_k2sCbhJOvGd4%2Bwm%2B3H8em5fJnS8xO7%2B1w%40mail.gmail.com > <https://groups.google.com/d/msgid/beancount/CACDkcs-w4K8p3a2v_k2sCbhJOvGd4%2Bwm%2B3H8em5fJnS8xO7%2B1w%40mail.gmail.com?utm_medium=email&utm_source=footer> > . > -- You received this message because you are subscribed to the Google Groups "Beancount" group. To unsubscribe from this group and stop receiving emails from it, send an email to beancount+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/beancount/CAFXPr0vEf-e0CjmGxTSzBmgXeGpi2csN_bxJdKDXhMWkn5vB4A%40mail.gmail.com.
