I would like to re-share links to our free vtreat data preparation system introduction videos, which show you what sort of machine learning problems vtreat can help you with. Python vtreat introduction video (PyData LA 2019), slides here. R vtreat introduction video (Why R? Foundation). The idea is: instead of […]
I would like to share a video where we show how to use the vtreat data transformer in the KNIME data science platform. (and we see there is an R/vtreat KNIME example here!)
Chapter 8 “Advanced Data Preparation” of Practical Data Science with R is a study in: Using the R vtreat package for advanced data preparation. Cross-validated data preparation. It is the professionally edited, ready to cite version of an important data preparation methodology. An advantage being: a number of well documented […]
Data science is often a case of brining the tools to the problems and data, instead of insisting on bringing the problems and data to the tools. To support cross-language data science we have been working on cross-language tools, documentation, and training.
Thank you very much Why R? for being awesome hosts. We are really pleased with how your virtual MeetUp went. For those who missed it here is a video link.
I would like to re-share vtreat (R version, Python version) a data preparation documentation for machine learning tasks. vtreat is a system for preparing messy real world data for predictive modeling tasks (classification, regression, and so on). In particular it is very good at re-coding high-cardinality string-valued (or categorical) variables […]
We have a new Win Vector data science article to share: Cross-Methods are a Leak/Variance Trade-Off John Mount (Win Vector LLC), Nina Zumel (Win Vector LLC) March 10, 2020 We work some exciting examples of when cross-methods (cross validation, and also cross-frames) work, and when they do not work. Abstract […]
vtreat version 1.5.2 just became available from CRAN. We have a logged a few improvement in the NEWS. The changes are small and incremental, as the package is already in a great stable state for production use.
One reason we are developing the wrapr to/unpack methods is the following: we wanted to spruce up the R vtreat interface a bit.
We’ve been experimenting with this for a while, and the next R vtreat package will have a back-port of the Python vtreat package sklearn pipe step interface (in addition to the standard R interface).