Scikit-learn’s Pipeline class is designed as a manageable way to apply a series of data transformations followed by a the application of an estimator.
Currently, my team and I are building a Shiny app that serves as an interface for a forecasting model. The app allows business users to interact with predictions. However, we keep getting feature requests, such as, “Can we please have this exported to Excel.” Our client chose to see results exported to a csv file and wants to open them in Excel. App is already running on the Linux server and the csv that can be downloaded via app are utf-8 encoded. If you are a Linux user you may not be aware that Windows Excel is not able to recognize utf-8 encoding automatically. It turns out that a few people faced this problem in the past. Obviously, we cannot have a solution where our users are changing options in Excel or opening the file in any other way than double clicking. We find having a Shiny App that allows for Excel export to be a good compromise between R/Shiny and Excel. It gives the user the power of interactivity and online access, while still preserving the possibility to work with the results in the environment they are most used to. This a great way to gradually accustom users with working in Shiny.
Pascale Fung explains how emotional interaction is being integrated into machines.
Talk about Bayesian modelling and choosing (informative) priors in the rstanarm-package.