  We look at common problems found in data that is used for predictive modeling
tasks, and describe how to address them with the vtreat R package. vtreat
prepares real-world data for predictive modeling in a reproducible and
statistically sound manner. We describe the theory of preparing variables so
that data has fewer exceptional cases, making it easier to safely use models in
production. Common problems dealt with include: infinite values, invalid
values, NA, too many categorical levels, rare categorical levels, and new
categorical levels (levels seen during application, but not during training).
Of special interest are techniques needed to avoid needlessly introducing
undesirable nested modeling bias (which is a risk when using a
data-preprocessor).
