[This article was first published on R – Win-Vector Blog, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
A data.frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. vtreat prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems vtreat defends against: Inf, NA, too many categorical levels, rare categorical levels, and new categorical levels (levels seen during application, but not during training). Reference: “vtreat: a data.frame Processor for Predictive Modeling”, Zumel, Mount, 2016.
To help you get up to speed we have a number of short worked examples of supervised machine learning using vtreathere. For instance here we work the KDD2009 example.