apply.factor.grouping | Apply groupings to new data |
classify.columns | Get variables class information |
compute.woe | Create cross-tabulation between factor and binary target |
convert.columns | Convert columns |
create.woe | Create WOE mapping |
cv.glm | Compute average AUC with cross-validation |
find.optimal.subset | Beta |
generate.date.features | Generate date features |
get.auc.importance | Calculate AUC importance of each attribute |
get.chisq.importance | Calculate Chi-squared importance of each attribute |
get.date.columns | Get names of all date columns |
get.date.format | Deduct date format |
get.factor.columns | Get names of all factor columns |
get.numeric.columns | Get names of all numeric columns |
get.proposed.conversions | Get proposed type conversions |
get.rf.importance | Calculate RandomForest importance of each attribute |
get.rf.subset.quality | Beta |
get.var.importance | Calculate combined importance measure of each attribute |
group.factor | Put rare factor values into separate group |
optimal.factor.grouping | Optimal factor cutoff (Beta) |
set.missing.factors.to.NA | Replace empty factor values with "NA" |
set.missing.to.mean | Replace numeric missing values with mean |
set.missing.to.prediction | Predict missing numeric values (beta) |
set.missing.to.random | Set missing to random |
set.missing.to.zero | Replace numeric missing values with zero |
standardize.columns | Standardize columns |
woe.apply | Apply WOE mapping to new data |
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