Information value

Source: R/fn_exp_categorical.R

Provides information value for each categorical variable (X) against target variable (Y)

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"

See also

IV

Examples

X = mtcars$gear
Y = mtcars$am
ExpInfoValue(X,Y,valueOfGood = 1)

#> $`Information values`
#> [1] 0.44
#> 
#> $`Predictive class`
#> [1] "Highly Predictive"
#>