A client recently came to us with a question: what’s a good way to monitor data or model output for changes? That is, how can you tell if new data is distributed differently from previous data, or if the distribution of scores returned by a model have changed? This client, like many others who have faced the same problem, simply checked whether the mean and standard deviation of the data had changed more than some amount, where the threshold value they checked against was selected in a more or less ad-hoc manner. But they were curious whether there was some other, perhaps more principled way, to check for a change in distribution.
Please check it out!
We have a new data scientist sticker!
If you see Nina or John at a conference/MeetUp, please ask us for a sticker!
For the next version of the R package wrapr we are going to be removing a number of under-used functions/methods and classes. This update will likely happen in March 2020, and is the start of the wrapr 2.* series.
Most of the items being removed are different abstractions for helping with function composition. We ended up moving most of our work to category-theory based composition, so don’t think these various frameworks are needed any longer. If you have been using these items in your own projects, please reach out and we try and find a way to help you out.
In a lot of our R writing we casually say “install from CRAN using
install.packages('PKGNAME')” or “update your packages by using
update.packages(ask = FALSE, checkBuilt = TRUE) (and answering ‘no’ to all questions about compiling).”
We recently became aware that for some users this isn’t complete advice.