Provides information value for each categorical variable (X) against target variable (Y)
Usage
ExpInfoValue(X, Y, valueOfGood = NULL)
Arguments
X
Independent categorical variable.
Y
Binary response variable, it can take values of either 1 or 0.
valueOfGood
Value of Y that is used as reference category.
Value
Information value (iv) and Predictive power class
information value
predictive class
Details
Information value is one of the most useful technique to select important variables in a predictive model. It helps to rank variables on the basis of their importance. The IV is calculated using the following formula
IV - (Percentage of Good event - Percentage of Bad event) * WOE, where WOE is weight of evidence
WOE - log(Percentage of Good event - Percentage of Bad event)
Here is what the values of IV mean according to Siddiqi (2006)
If information value is < 0.03 then predictive power = "Not Predictive"
If information value is 0.03 to 0.1 then predictive power = "Somewhat Predictive"
If information value is 0.1 to 0.3 then predictive power = "Meidum Predictive"
If information value is >0.3 then predictive power = "Highly Predictive"