statistical-iv 0.3.1

Description:

statisticaliv 0.3.1

Statistical IV
Our J-Divergence test is under the next null hypothesis
H0: The predictive power of the variable is not significant.
The null hypothesis is tested using a two-tailed distribution, and this should be taken into consideration when interpreting the p-value.
Explanation
Optimize your machine learning models with 'Statistical-IV'. Perform automated feature selection based on statistics and customize error control.


Import package
from statistical_iv import api



Provide a DataFrame as Input:

Supply a DataFrame df containing your data for IV calculation.



Specify Predictor Variables:

Prived a list of predictor variable names (variables_names) to analyze.



Define the Target Variable:

Specify the name of the target variable (var_y) in your DataFrame.



Indicate Variable Types:

Define the type of your predictor variables as 'categorical' or 'numerical' using the type_vars parameter.



Optional: Set Maximum Bins:

Adjust the maximum number of bins for discretization (optional) using the max_bins parameter.



Call the statistical_iv Function:

Calculate Statistical IV information by calling the statistical_iv function from api with the specified parameters (That is used for OptimalBinning package).

result_df = api.statistical_iv(df, variables_names, var_y, type_vars, max_bins)



Example Result:

Full Paper:
For a comprehensive exploration of the topic, we recommend perusing the contents of the article available at this link.