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). 'vtreat::prepare' should be used as you would use 'model.matrix'.
|Author||John Mount, Nina Zumel|
|Date of publication||2017-06-14 01:37:36 UTC|
|Maintainer||John Mount <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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