scorecard (version 0.1.5)

iv: Information Value

Description

This function calculates information value (IV) for multiple x variables.

Usage

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. Default NULL If x is NULL, all variables exclude y will counted as x variables.

positive

Value of positive class, default "bad|1".

order

Logical. If it is TRUE, return descending sorted iv values.

Value

Information Value

Details

IV is a very useful concept for variable selection while developing credit scorecards. The formula for information value is shown below: $$IV = \sum(DistributionBad_{i} - DistributionGood_{i})*\ln(\frac{DistributionBad_{i}}{DistributionGood_{i}}).$$ The log component in information value is defined as weight of evidence (WOE), which is shown as $$WeightofEvidence = \ln(\frac{DistributionBad_{i}}{DistributionGood_{i}}).$$ The relationship between information value and predictive power is as follows: <0.02 (useless for prediction), 0.02 to 0.1 (Weak predictor), 0.1 to 0.3 (Medium predictor), 0.3 to 0.5 (Strong predictor) and >0.5 (Suspicious or too good to be true).

Examples

# NOT RUN {
# Load German credit data
data(germancredit)

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

# }