Statistical_IV: J-Divergence Hypothesis Test for the Information Value (IV)
Project description
Statistical IV
Statistical_IV: J-Divergence Hypothesis Test for the Information Value (IV). Calculation of the Information Value with specific limits to the predictive power.
Using optimalBinning, We created a specific way to calculate a predicitive power for each particular variable.
-
Provide a DataFrame as Input:
- Supply a DataFrame
df
containing your data for IV calculation.
- Supply a DataFrame
-
Specify Predictor Variables:
- Prived a list of predictor variable names (
variables_names
) to analyze.
- Prived a list of predictor variable names (
-
Define the Target Variable:
- Specify the name of the target variable (
var_y
) in your DataFrame.
- Specify the name of the target variable (
-
Indicate Variable Types:
- Define the type of your predictor variables as 'categorical' or 'numerical' using the
type_vars
parameter.
- Define the type of your predictor variables as 'categorical' or 'numerical' using the
-
Optional: Set Maximum Bins:
- Adjust the maximum number of bins for discretization (optional) using the
max_bins
parameter.
- Adjust the maximum number of bins for discretization (optional) using the
-
Call the
statistical_iv
Function:- Calculate IV by calling the
statistical_iv
function with the specified parameters (That is used for OptimalBinning package).
result_df = statistical_iv(df, variables_names, var_y, type_vars, max_bins)
- Calculate IV by calling the
Example Result:
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