Value variables for prediction a categorical outcome.
value_variables_C( dframe, varlist, outcomename, outcometarget, ..., weights = c(), minFraction = 0.02, smFactor = 0, rareCount = 0, rareSig = 1, collarProb = 0, scale = FALSE, doCollar = FALSE, splitFunction = NULL, ncross = 3, forceSplit = FALSE, catScaling = TRUE, verbose = FALSE, parallelCluster = NULL, use_parallel = TRUE, customCoders = list(c.PiecewiseV.num = vtreat::solve_piecewisec, n.PiecewiseV.num = vtreat::solve_piecewise, c.knearest.num = vtreat::square_windowc, n.knearest.num = vtreat::square_window), codeRestriction = c("PiecewiseV", "knearest", "clean", "isBAD", "catB", "catP"), missingness_imputation = NULL, imputation_map = NULL )
Data frame to learn treatments from (training data), must have at least 1 row.
Names of columns to treat (effective variables).
Name of column holding outcome variable. dframe[[outcomename]] must be only finite non-missing values.
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
optional training weights for each row
optional minimum frequency a categorical level must have to be converted to an indicator column.
optional smoothing factor for impact coding models.
optional integer, allow levels with this count or below to be pooled into a shared rare-level. Defaults to 0 or off.
optional numeric, suppress levels from pooling at this significance value greater. Defaults to NULL or off.
what fraction of the data (pseudo-probability) to collar data at if doCollar is set during
optional if TRUE replace numeric variables with regression ("move to outcome-scale").
optional if TRUE collar numeric variables by cutting off after a tail-probability specified by collarProb during treatment design.
(optional) see vtreat::buildEvalSets .
optional scalar>=2 number of cross-validation rounds to design.
logical, if TRUE force cross-validated significance calculations on all variables.
optional, if TRUE use glm() linkspace, if FALSE use lm() for scaling.
if TRUE print progress.
(optional) a cluster object created by package parallel or package snow.
logical, if TRUE use parallel methods.
additional coders to use for variable importance estimate.
codes to restrict to for variable importance estimate.
function of signature f(values: numeric, weights: numeric), simple missing value imputer.
map from column names to functions of signature f(values: numeric, weights: numeric), simple missing value imputers.
table of variable valuations