vtreat
is a data.frame
processor/conditioner (available for
R
, and for
Python
) that prepares
realworld data for supervised machine learning or predictive modeling
in a statistically sound manner.
vtreat
takes an input data.frame
that has a specified column called
“the outcome variable” (or “y”) that is the quantity to be predicted
(and must not have missing values). Other input columns are possible
explanatory variables (typically numeric or categorical/stringvalued,
these columns may have missing values) that the user later wants to use
to predict “y”. In practice such an input data.frame
may not be
immediately suitable for machine learning procedures that often expect
only numeric explanatory variables, and may not tolerate missing values.
To solve this, vtreat
builds a transformed data.frame
where all
explanatory variable columns have been transformed into a number of
numeric explanatory variable columns, without missing values. The
vtreat
implementation produces derived numeric columns that capture
most of the information relating the explanatory columns to the
specified “y” or dependent/outcome column through a number of numeric
transforms (indicator variables, impact codes, prevalence codes, and
more). This transformed data.frame
is suitable for a wide range of
supervised learning methods from linear regression, through gradient
boosted machines.
The idea is: you can take a data.frame
of messy real world data and
easily, faithfully, reliably, and repeatably prepare it for machine
learning using documented methods using vtreat
. Incorporating vtreat
into your machine learning workflow lets you quickly work with very
diverse structured data.
In all cases (classification, regression, unsupervised, and multinomial
classification) the intent is that vtreat
transforms are essentially
one liners.
The preparation commands are organized as follows:

Regression:
R
regression example,Python
regression example. 
Classification:
R
classification example,Python
classification example. 
Unsupervised tasks:
R
unsupervised example,Python
unsupervised example. 
Multinomial classification:
R
multinomial classification example,Python
multinomial classification example.
In all cases: variable preperation is intended to be a “one liner.”
These current revisions of the examples are designed to be small, yet complete. So as a set they have some overlap, but the user can rely mostly on a single example for a single task type.
For more detail please see here: arXiv:1611.09477
stat.AP (the documentation describes
the R
version, however all of the examples can be found worked in
Python
here).
vtreat
is available as an R
package, and also as a
Python
/Pandas
package.
(logo: Julie Mount, source: “The Harvest” by Boris Kustodiev 1914)
Even with modern machine learning techniques (random forests, support vector machines, neural nets, gradient boosted trees, and so on) or standard statistical methods (regression, generalized regression, generalized additive models) there are common data issues that can cause modeling to fail. vtreat deals with a number of these in a principled and automated fashion.
In particular vtreat emphasizes a concept called “yaware preprocessing” and implements:
 Treatment of missing values through safe replacement plus indicator column (a simple but very powerful method when combined with downstream machine learning algorithms).
 Treatment of novel levels (new values of categorical variable seen during test or application, but not seen during training) through submodels (or impact/effects coding of pooled rare events).
 Explicit coding of categorical variable levels as new indicator variables (with optional suppression of nonsignificant indicators).
 Treatment of categorical variables with very large numbers of levels through submodels (again impact/effects coding).
 (optional) User specified significance pruning on levels coded into effects/impact submodels.
 Correct treatment of nested models or submodels through data split (see here) or through the generation of “cross validated” data frames (see here); these are issues similar to what is required to build statistically efficient stacked models or superlearners).
 Safe processing of “wide data” (data with very many variables, often driving common machine learning algorithms to overfit) through out of sample pervariable significance estimates and user controllable pruning (something we have lectured on previously here and here).
 Collaring/Winsorizing of unexpected out of range numeric inputs.
 (optional) Conversion of all variables into effects (or “yscale”)
units (through the optional
scale
argument tovtreat::prepare()
, using some of the ideas discussed here). This allows correct/sensible application of principal component analysis preprocessing in a machine learning context.  Joining in additional training distribution data (which can be useful in analysis, called “catP” and “catD”).
The idea is: even with a sophisticated machine learning algorithm there are many ways messy real world data can defeat the modeling process, and vtreat helps with at least ten of them. We emphasize: these problems are already in your data, you simply build better and more reliable models if you attempt to mitigate them. Automated processing is no substitute for actually looking at the data, but vtreat supplies efficient, reliable, documented, and tested implementations of many of the commonly needed transforms.
To help explain the methods we have prepared some documentation:
 The vtreat package overall.
 Preparing data for analysis using R whitepaper
 The types of new variables introduced by vtreat processing (including how to limit down to domain appropriate variable types).
 Statistically sound treatment of the nested modeling issue introduced by any sort of preprocessing (such as vtreat itself): nested overfit issues and a general crossframe solution.
 Principled ways to pick significance based pruning levels.
Data treatments are “yaware” (use distribution relations between
independent variables and the dependent variable). For binary
classification use designTreatmentsC()
and for numeric regression use
designTreatmentsN()
.
After the design step, prepare()
should be used as you would use
model.matrix. prepare()
treated variables are all numeric and never
take the value NA or +Inf (so are very safe to use in modeling).
In application we suggest splitting your data into three sets: one for building vtreat encodings, one for training models using these encodings, and one for test and model evaluation.
The purpose of vtreat
library is to reliably prepare data for
supervised machine learning. We try to leave as much as possible to the
machine learning algorithms themselves, but cover most of the truly
necessary typically ignored precautions. The library is designed to
produce a data.frame
that is entirely numeric and takes common
precautions to guard against the following real world data issues:

Categorical variables with very many levels.
We reencode such variables as a family of indicator or dummy variables for common levels plus an additional impact code (also called “effects coded”). This allows principled use (including smoothing) of huge categorical variables (like zipcodes) when building models. This is critical for some libraries (such as
randomForest
, which has hard limits on the number of allowed levels). 
Rare categorical levels.
Levels that do not occur often during training tend not to have reliable effect estimates and contribute to overfit. vtreat helps with 2 precautions in this case. First the
rareLevel
argument suppresses levels with this count our below from modeling, except possibly through a grouped contribution. Also with enough data vtreat attempts to estimate out of sample performance of derived variables. Finally we suggest users reserve a portion of data for vtreat design, separate from any data used in additional training, calibration, or testing. 
Novel categorical levels.
A common problem in deploying a classifier to production is: new levels (levels not seen during training) encountered during model application. We deal with this by encoding categorical variables in a possibly redundant manner: reserving a dummy variable for all levels (not the more common all but a reference level scheme). This is in fact the correct representation for regularized modeling techniques and lets us code novel levels as all dummies simultaneously zero (which is a reasonable thing to try). This encoding while limited is cheaper than the fully Bayesian solution of computing a weighted sum over previously seen levels during model application.

Missing/invalid values NA, NaN, +Inf.
Variables with these issues are recoded as two columns. The first column is clean copy of the variable (with missing/invalid values replaced with either zero or the grand mean, depending on the user chose of the
scale
parameter). The second column is a dummy or indicator that marks if the replacement has been performed. This is simpler than imputation of missing values, and allows the downstream model to attempt to use missingness as a useful signal (which it often is in industrial data). 
Extreme values.
Variables can be restricted to stay in ranges seen during training. This can defend against some runaway classifier issues during model application.

Constant and nearconstant variables.
Variables that “don’t vary” or “nearly don’t vary” are suppressed.

Need for estimated singlevariable model effect sizes and significances.
It is a dirty secret that even popular machine learning techniques need some variable pruning (when exposed to very wide data frames, see here and here). We make the necessary effect size estimates and significances easily available and supply initial variable pruning.
The above are all awful things that often lurk in real world data.
Automating these steps ensures they are easy enough that you actually
perform them and leaves the analyst time to look for additional data
issues. For example this allowed us to essentially automate a number of
the steps taught in chapters 4 and 6 of Practical Data Science with R
(Zumel, Mount; Manning 2014) into a
very short
worksheet (though we
think for understanding it is essential to work all the steps by hand
as we did in the book). The 2nd edition of Practical Data Science with
R covers using vtreat
in R
in chapter 8 “Advanced Data
Preparation.”
The idea is: data.frame
s prepared with the vtreat
library are
somewhat safe to train on as some precaution has been taken against all
of the above issues. Also of interest are the vtreat
variable
significances (help in initial variable pruning, a necessity when there
are a large number of columns) and vtreat::prepare(scale=TRUE)
which
reencodes all variables into effect units making them suitable for
yaware dimension reduction (variable clustering, or principal component
analysis) and for geometry sensitive machine learning techniques
(kmeans, knn, linear SVM, and more). You may want to do more than the
vtreat
library does (such as Bayesian imputation, variable clustering,
and more) but you certainly do not want to do less.
There have been a number of recent substantial improvements to the library, including:
 Out of sample scoring.
 Ability to use
parallel
.  More general calculation of effect sizes and significances.
Some of our related articles (which should make clear some of our motivations, and design decisions):
 Modeling trick: impact coding of categorical variables with many levels
 A bit more on impact coding
 vtreat: designing a package for variable treatment
 A comment on preparing data for classifiers
 Nina Zumel presenting on vtreat
 What is new in the vtreat library?
 How do you know if your data has signal?
Examples of current best practice using vtreat
(variable coding,
train, test split) can be found
here
and here.
Trivial example:
library("vtreat")
packageVersion("vtreat")
# [1] '1.4.8'
citation('vtreat')
#
# To cite package 'vtreat' in publications use:
#
# John Mount and Nina Zumel (2019). vtreat: A Statistically Sound
# 'data.frame' Processor/Conditioner.
# https://github.com/WinVector/vtreat/,
# https://winvector.github.io/vtreat/.
#
# A BibTeX entry for LaTeX users is
#
# @Manual{,
# title = {vtreat: A Statistically Sound 'data.frame' Processor/Conditioner},
# author = {John Mount and Nina Zumel},
# year = {2019},
# note = {https://github.com/WinVector/vtreat/, https://winvector.github.io/vtreat/},
# }
# categorical example
dTrainC < data.frame(x=c('a', 'a', 'a', 'b', 'b', NA, NA),
z=c(1, 2, 3, 4, NA, 6, NA),
y=c(FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE))
dTestC < data.frame(x=c('a', 'b', 'c', NA), z=c(10, 20, 30, NA))
# help("designTreatmentsC")
treatmentsC < designTreatmentsC(dTrainC, colnames(dTrainC), 'y', TRUE,
verbose=FALSE)
print(treatmentsC$scoreFrame[, c('origName', 'varName', 'code', 'rsq', 'sig', 'extraModelDegrees')])
# origName varName code rsq sig extraModelDegrees
# 1 x x_catP catP 0.060049677 0.44862725 2
# 2 x x_catB catB 0.127625394 0.26932340 2
# 3 z z clean 0.237601767 0.13176020 0
# 4 z z_isBAD isBAD 0.296065432 0.09248399 0
# 5 x x_lev_NA lev 0.296065432 0.09248399 0
# 6 x x_lev_x_a lev 0.130005705 0.26490379 0
# 7 x x_lev_x_b lev 0.006067337 0.80967242 0
# help("prepare")
dTrainCTreated < prepare(treatmentsC, dTrainC, pruneSig=1.0, scale=TRUE)
varsC < setdiff(colnames(dTrainCTreated), 'y')
# all input variables should be mean 0
sapply(dTrainCTreated[, varsC, drop=FALSE], mean)
# x_catP x_catB z z_isBAD x_lev_NA
# 2.537498e16 1.268826e16 6.336166e17 2.536414e16 2.537653e16
# x_lev_x_a x_lev_x_b
# 6.345680e17 1.189718e17
# all non NA slopes should be 1
sapply(varsC, function(c) { lm(paste('y', c, sep='~'),
data=dTrainCTreated)$coefficients[[2]]})
# x_catP x_catB z z_isBAD x_lev_NA x_lev_x_a
# 0.23254609 0.05841932 0.16062145 0.03162633 0.03162633 0.23254609
# x_lev_x_b
# 0.24663035
dTestCTreated < prepare(treatmentsC, dTestC, pruneSig=c(), scale=TRUE)
print(dTestCTreated)
# x_catP x_catB z z_isBAD x_lev_NA x_lev_x_a x_lev_x_b
# 1 1.0238626 3.248380 7.437329 5.420438 5.420438 1.0238626 0.1158472
# 2 0.7678969 2.550396 18.374578 5.420438 5.420438 0.7678969 0.2896179
# 3 3.4555361 2.260694 29.311827 5.420438 5.420438 0.7678969 0.1158472
# 4 0.7678969 7.422967 0.000000 13.551095 13.551095 0.7678969 0.1158472
# numeric example
dTrainN < data.frame(x=c('a', 'a', 'a', 'a', 'b', 'b', NA, NA),
z=c(1, 2, 3, 4, 5, NA, 7, NA), y=c(0, 0, 0, 1, 0, 1, 1, 1))
dTestN < data.frame(x=c('a', 'b', 'c', NA), z=c(10, 20, 30, NA))
# help("designTreatmentsN")
treatmentsN = designTreatmentsN(dTrainN, colnames(dTrainN), 'y',
verbose=FALSE)
print(treatmentsN$scoreFrame[, c('origName', 'varName', 'code', 'rsq', 'sig', 'extraModelDegrees')])
# origName varName code rsq sig extraModelDegrees
# 1 x x_catP catP 2.869955e01 0.1711651 2
# 2 x x_catN catN 2.014886e02 0.7374034 2
# 3 x x_catD catD 3.614228e01 0.1149323 2
# 4 z z clean 2.880952e01 0.1701892 0
# 5 z z_isBAD isBAD 3.333333e01 0.1339746 0
# 6 x x_lev_NA lev 3.333333e01 0.1339746 0
# 7 x x_lev_x_a lev 2.500000e01 0.2070312 0
# 8 x x_lev_x_b lev 1.110223e16 1.0000000 0
dTrainNTreated < prepare(treatmentsN, dTrainN, pruneSig=1.0, scale=TRUE)
varsN < setdiff(colnames(dTrainNTreated), 'y')
# all input variables should be mean 0
sapply(dTrainNTreated[, varsN, drop=FALSE], mean)
# x_catP x_catN x_catD z z_isBAD
# 2.775558e17 0.000000e+00 2.775558e17 4.857226e17 6.938894e18
# x_lev_NA x_lev_x_a x_lev_x_b
# 6.938894e18 0.000000e+00 7.703720e34
# all non NA slopes should be 1
sapply(varsN, function(c) { lm(paste('y', c, sep='~'),
data=dTrainNTreated)$coefficients[[2]]})
# x_catP x_catN x_catD z z_isBAD x_lev_NA x_lev_x_a
# 1 1 1 1 1 1 1
# x_lev_x_b
# 1
dTestNTreated < prepare(treatmentsN, dTestN, pruneSig=c(), scale=TRUE)
print(dTestNTreated)
# x_catP x_catN x_catD z z_isBAD x_lev_NA x_lev_x_a
# 1 0.250 0.25 0.06743804 0.9952381 0.1666667 0.1666667 0.25
# 2 0.250 0.00 0.25818161 2.5666667 0.1666667 0.1666667 0.25
# 3 0.625 0.00 0.25818161 4.1380952 0.1666667 0.1666667 0.25
# 4 0.250 0.50 0.39305768 0.0000000 0.5000000 0.5000000 0.25
# x_lev_x_b
# 1 2.266233e17
# 2 6.798700e17
# 3 2.266233e17
# 4 2.266233e17
# for large data sets you can consider designing the treatments on
# a subset like: d[sample(1:dim(d)[[1]], 1000), ]
# One can also use treatment plans as pipe targets.
dTrainN %.>%
treatmentsN %.>%
knitr::kable(.)
x_catP  x_catN  x_catD  z  z_isBAD  x_lev_NA  x_lev_x_a  x_lev_x_b  y 

0.50  0.25  0.5000000  1.000000  0  0  1  0  0 
0.50  0.25  0.5000000  2.000000  0  0  1  0  0 
0.50  0.25  0.5000000  3.000000  0  0  1  0  0 
0.50  0.25  0.5000000  4.000000  0  0  1  0  1 
0.25  0.00  0.7071068  5.000000  0  0  0  1  0 
0.25  0.00  0.7071068  3.666667  1  0  0  1  1 
0.25  0.50  0.0000000  7.000000  0  1  0  0  1 
0.25  0.50  0.0000000  3.666667  1  1  0  0  1 
Related work:
 Cohen J, Cohen P (1983). Applied Multiple Regression/Correlation Analysis For The Behavioral Sciences. 2 edition. Lawrence Erlbaum Associates, Inc. ISBN 0898592682.
 “A preprocessing scheme for highcardinality categorical attributes in classification and prediction problems” Daniele MicciBarreca; ACM SIGKDD Explorations, Volume 3 Issue 1, July 2001 Pages 2732.
 “Modeling Trick: Impact Coding of Categorical Variables with Many Levels” Nina Zumel; WinVector blog, 2012.
 “Big Learning Made Easy – with Counts!”, Misha Bilenko, Cortana Intelligence and Machine Learning Blog, 2015.
Installation
To install, from inside R
please run:
install.packages("vtreat")
Note
Notes on controlling vtreat
’s crossvalidation plans can be found
here.
Note: vtreat
is meant only for “tame names”, that is: variables and
column names that are also valid simple (without quotes) R
variables
names.