Compute the WOETable that shows the Weights Of Evidence (WOE) for each group and respeective Information Values (IVs).
WOETable(X, Y, valueOfGood = 1)
The categorical variable stored as factor for which WOE Table is to be computed.
The actual 1/0 flags for the binary response variable. It can take values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'.
The value in Y that is used to represent 'Good' or the occurence of the event of interest. Defaults to 1.
For a given actual for a Binary Y variable and a categorical X variable stored as factor, the WOE table is generated with calculated WOE's and IV's
The WOE table with the respective weights of evidence for each group and the IV's.
CAT. The groups (levels) of the categorical X variable for which WOE is to be calculated.
GOODS. The total number of "Goods" or "Events" in respective group.
BADS. The total number of "Bads" or "Non-Events" in respective group.
TOTAL. The total number of observations in respective group.
PCT_G. The Percentage of 'Goods' or 'Events' accounted for by respective group.
PCT_B. The Percentage of 'Bads' or 'Non-Events' accounted for by respective group.
WOE. The computed weights of evidence(WOE) for respective group. The WOE values can be used in place of the actual group itself, thereby producing a 'continuous' alternative.
IV. The information value contributed by each group in the X. The sum of IVs is the total information value of the categorical X variable.
Selva Prabhakaran email@example.com
data('SimData') WOETable(X=SimData$X.Cat, Y=SimData$Y.Binary)
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