One-Hot-Encoding Categorical Variables

Supervised Learning in R: Regression

Nina Zumel and John Mount

Win-Vector, LLC

Why Convert Categoricals Manually?

Most R functions manage the conversion for you
- model.matrix()
xgboost() does not
- Must convert categorical variables to numeric representation
Conversion to indicators: one-hot encoding

One-hot-encoding and data cleaning with `vtreat`

Basic idea:

designTreatmentsZ() to design a treatment plan from the training data, then
prepare() to created "clean" data
- all numerical
- no missing values
  - use prepare() with treatment plan for all future data

A Small vtreat Example

Training Data

x	u	y
one	44	0.4855671
two	24	1.3683726
three	66	2.0352837
two	22	1.6396267

Test Data

x	u	y
one	5	2.6488148
three	12	1.5012938
one	56	0.1993731
two	28	1.2778516

Create the Treatment Plan

vars <- c("x", "u")
treatplan <- designTreatmentsZ(dframe, varslist, verbose = FALSE)

Inputs to designTreatmentsZ()

dframe: training data
varlist: list of input variable names
set verbose = FALSE to suppress progress messages

Get the New Variables

The scoreFrame describes the variable mapping and types

(scoreFrame <- treatplan$scoreFrame %>% 
     select(varName, origName, code))

        varName origName  code
1   x_lev_x.one        x   lev
2 x_lev_x.three        x   lev
3   x_lev_x.two        x   lev
4        x_catP        x  catP
5       u_clean        u clean

Get the names of the new lev and clean variables

(newvars <- scoreFrame %>% 
     filter(code %in% c("clean", "lev")) %>%
     use_series(varName))

"x_lev_x.one"   "x_lev_x.three" "x_lev_x.two"   "u_clean"

Prepare the Training Data for Modeling

training.treat <- prepare(treatmentplan, dframe, varRestriction = newvars)

Inputs to prepare():

treatmentplan: treatment plan
dframe: data frame
varRestriction: list of variables to prepare (optional)
- default: prepare all variables

Before and After Data Treatment

Training Data

x	u	y
one	44	0.4855671
two	24	1.3683726
three	66	2.0352837
two	22	1.6396267

Treated Training Data

x_lev _x. one	x_lev _x. three	x_lev _x. two	u_clean
1	0	0	44
0	0	1	24
0	1	0	66
0	0	1	22

Prepare the Test Data Before Model Application

(test.treat <- prepare(treatplan, test, varRestriction = newvars))

  x_lev_x.one x_lev_x.three x_lev_x.two u_clean
1           1             0           0       5
2           0             1           0      12
3           1             0           0      56
4           0             0           1      28

vtreat Treatment is Robust

Previously unseen x level: four

x	u	y
one	4	0.2331301
two	14	1.9331760
three	66	3.1251029
four	25	4.0332491

four encodes to (0, 0, 0)

prepare(treatplan, toomany, ...)

x_lev _x. one	x_lev _x. three	x_lev _x. two	u_clean
1	0	0	4
0	0	1	14
0	1	0	66
0	0	0	25

Let's practice!

Supervised Learning in R: Regression