vtreat is a powerful
R package for preparing messy real-world data for machine learning. We have further extended the package with a number of features including rquery/rqdatatable integration (allowing vtreat application at scale on Apache Spark or data.table!).
vtreat and can now effectively prepare data for multi-class classification or multinomial modeling.
Continue reading Modeling muti-category Outcomes With vtreat
Data preparation and cleaning are some of the most important steps of predictive analytic and data science tasks. They are laborious, where most of the errors are made, your last line of defense against a wild data, and hold the biggest opportunities for outcome improvement. No matter how much time you spend on them, they still seem like a neglected topic. Data preparation isn’t as self contained or genteel as tweaking machine learning models or hyperparameter tuning; and that is one of the reasons data preparation represents such an important practical opportunity for improvement.
Photo: NY – http://nyphotographic.com/, License: Creative Commons 3 – CC BY-SA 3.0
Our group is distributing a detailed writeup of the theory and operation behind our R realization of a set of sound data preparation and cleaning procedures called vtreat here: arXiv:1611.09477 [stat.AP]. This is where you can find out what
vtreat does, decide if it is appropriate for your problem, or even find a specification allowing the use of the techniques in non-
R environments (such as
Spark, and many others).
We have submitted this article for formal publication, so it is our intent you can cite this article (as it stands) in scientific work as a pre-print, and later cite it from a formally refereed source.
Or alternately, below is the tl;dr (“too long; didn’t read”) form. Continue reading Data Preparation, Long Form and tl;dr Form