designTreatmentsC {vtreat}

Build all treatments for a data frame to predict a categorical outcome.

Description

Function to design variable treatments for binary prediction of a categorical outcome. Data frame is assumed to have only atomic columns except for dates (which are converted to numeric). Note: re-encoding high cardinality categorical variables can introduce undesirable nested model bias, for such data consider using mkCrossFrameCExperiment.

Usage

designTreatmentsC(
  dframe,
  varlist,
  outcomename,
  outcometarget = TRUE,
  ...,
  weights = c(),
  minFraction = 0.02,
  smFactor = 0,
  rareCount = 0,
  rareSig = NULL,
  collarProb = 0,
  codeRestriction = NULL,
  customCoders = NULL,
  splitFunction = NULL,
  ncross = 3,
  forceSplit = FALSE,
  catScaling = TRUE,
  verbose = TRUE,
  parallelCluster = NULL,
  use_parallel = TRUE,
  missingness_imputation = NULL,
  imputation_map = NULL
)

Arguments

dframe

Data frame to learn treatments from (training data), must have at least 1 row.

varlist

Names of columns to treat (effective variables).

outcomename

Name of column holding outcome variable. dframe[[outcomename]] must be only finite non-missing values.

outcometarget

Value/level of outcome to be considered "success", and there must be a cut such that dframe[[outcomename]]==outcometarget at least twice and dframe[[outcomename]]!=outcometarget at least twice.

...

no additional arguments, declared to forced named binding of later arguments

weights

optional training weights for each row

minFraction

optional minimum frequency a categorical level must have to be converted to an indicator column.

smFactor

optional smoothing factor for impact coding models.

rareCount

optional integer, allow levels with this count or below to be pooled into a shared rare-level. Defaults to 0 or off.

rareSig

optional numeric, suppress levels from pooling at this significance value greater. Defaults to NULL or off.

collarProb

what fraction of the data (pseudo-probability) to collar data at if doCollar is set during prepare.treatmentplan.

codeRestriction

what types of variables to produce (character array of level codes, NULL means no restriction).

customCoders

map from code names to custom categorical variable encoding functions (please see https://github.com/WinVector/vtreat/blob/main/extras/CustomLevelCoders.md).

splitFunction

(optional) see vtreat::buildEvalSets .

ncross

optional scalar >=2 number of cross validation splits use in rescoring complex variables.

forceSplit

logical, if TRUE force cross-validated significance calculations on all variables.

catScaling

optional, if TRUE use glm() linkspace, if FALSE use lm() for scaling.

verbose

if TRUE print progress.

parallelCluster

(optional) a cluster object created by package parallel or package snow.

use_parallel

logical, if TRUE use parallel methods (when parallel cluster is set).

missingness_imputation

function of signature f(values: numeric, weights: numeric), simple missing value imputer.

imputation_map

map from column names to functions of signature f(values: numeric, weights: numeric), simple missing value imputers.

Details

The main fields are mostly vectors with names (all with the same names in the same order):

- vars : (character array without names) names of variables (in same order as names on the other diagnostic vectors) - varMoves : logical TRUE if the variable varied during hold out scoring, only variables that move will be in the treated frame - #' - sig : an estimate significance of effect

See the vtreat vignette for a bit more detail and a worked example.

Columns that do not vary are not passed through.

Note: re-encoding high cardinality on training data can introduce nested model bias, consider using mkCrossFrameCExperiment instead.

Value

treatment plan (for use with prepare)

See Also

prepare.treatmentplan, designTreatmentsN, designTreatmentsZ, mkCrossFrameCExperiment

Examples

dTrainC <- data.frame(x=c('a','a','a','b','b','b'),
   z=c(1,2,3,4,5,6),
   y=c(FALSE,FALSE,TRUE,FALSE,TRUE,TRUE))
dTestC <- data.frame(x=c('a','b','c',NA),
   z=c(10,20,30,NA))
treatmentsC <- designTreatmentsC(dTrainC,colnames(dTrainC),'y',TRUE)
dTestCTreated <- prepare(treatmentsC,dTestC,pruneSig=0.99)