Bryan Shalloway shows off some new functionality in the rsample package:
For some problems you may want to take a traditional regression or classification based approach while still accounting for the date/time-sensitive components of your data. In this post I will use the
tidymodels
suite of packages to:– build lag based and non-lag based features
– set-up appropriate time series cross-validation windows
– evaluate performance of linear regression and random forest models on a regression problemFor my example I will use data from Wake County food inspections. I will try to predict the
SCORE
for upcoming restaurant food inspections.
Click through to see it in action.
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