Data Preparation, Long Form and tl;dr Form

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 Python/Pandas/scikit-learn, 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.

Our concrete advice is: when building a supervised model (regression or classification) in R, prepare your training, test, and application data by doing the following.

# load the vtreat packagelibrary("vtreat")# use your training data to design # data treatment plance <- mkCrossFrameCExperiment(trainData,                               vars,                              yName, yTarget)# look at the variable scoresvarScores <- ce$treatments$scoreFrameprint(varScores)# prune variables based on significancepruneSig <- 1/nrow(varScores)modelVars <- varScores$varName[varScores$sig<=pruneSig]# instead of preparing training data, use # "simulated out of sample data" to reduce modeling biastreatedTrainData <- ce$crossFrame# prepare any other data (test, future application)# using the treatment plantreatedTestData <- prepare(ce$treatments,                            testData,                            varRestriction= modelVars,                            pruneSig= NULL)

Then work through our examples to find out what all these steps are doing for you.