scorecard (version 0.4.4)

iv: Information Value

Description

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.

Usage

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

Value

A data frame with columns for variable and info_value

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.

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)