This function calculates information value (IV) for multiple x variables. It treats each unique value in x variables as a group. If there is a zero number of y class, it will be replaced by 0.99 to make sure woe/iv is calculable.

iv(dt, y, x = NULL, positive = "bad|1", order = TRUE)

## Arguments

dt

A data frame with both x (predictor/feature) and y (response/label) variables.

y

Name of y variable.

x

Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables.

positive

Value of positive class, Defaults to "bad|1".

order

Logical, Defaults to TRUE. If it is TRUE, the output will descending order via iv.

## Value

A data frame with columns for variable and info_value

## Details

IV is a very useful concept for variable selection while developing credit scorecards. The formula for information value is shown below: $$IV = \sum(DistributionPositive_{i} - DistributionNegative_{i})*\ln(\frac{DistributionPositive_{i}}{DistributionNegative_{i}}).$$ The log component in information value is defined as weight of evidence (WOE), which is shown as $$WeightofEvidence = \ln(\frac{DistributionPositive_{i}}{DistributionNegative_{i}}).$$ The relationship between information value and predictive power is as follows:

 Information Value Predictive Power ----------------- ---------------- < 0.02 useless for prediction 0.02 to 0.1 Weak predictor 0.1 to 0.3 Medium predictor > 0.3 Strong predictor

## Examples

# Load German credit data
data(germancredit)

# information values
info_value = iv(germancredit, y = "creditability")

str(info_value)
#> Classes ‘data.table’ and 'data.frame':	20 obs. of  2 variables:
#>  $variable : chr "status.of.existing.checking.account" "duration.in.month" "credit.history" "age.in.years" ... #>$ info_value: num  0.666 0.335 0.293 0.26 0.196 ...
#>  - attr(*, ".internal.selfref")=<externalptr>