Performs exploratory data analysis and variable screening for binary classification models using weight-of-evidence (WOE) and information value (IV). In order to make the package as efficient as possible, aggregations are done in data.table and creation of WOE vectors can be distributed across multiple cores. The package also supports exploration for uplift models (NWOE and NIV).
|Author||Larsen Kim [aut, cre]|
|Date of publication||2016-04-09 00:24:08|
|Maintainer||Larsen Kim <email@example.com>|
|License||GPL (>= 3)|
Aggregate: (helper function )Aggregate data for WOE/NWOE calculations
CheckInputs: (helper function) Check user inputs and convert factors to...
create_infotables: Create WOE/NWOE tables and rank variables by IV/NIV
Information: Data exploration with information theory (weight-of-evidence...
is.binary: (helper function) Calculate cross validation penalty
MultiPlot: (helper function) Plot mutiple WOE vectors on one page
NWOE: Create WOE table (helper function)
penalty: (helper function) Calculate cross validation penalty
plot_infotables: Create bar charts for WOE or NWOE vectors
SinglePlot: (helper function) Plot a WOE or NWOE vector
train: Training dataset
valid: Validation dataset
WOE: Create WOE tables from aggregated data (helper function)