statistical-iv 0.3.2

Statistical IV: Statistical Hypothesis Testing for the Information Value (IV). Evaluation of the predictive power of features using the IV with specific thresholds for each dataset.

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.

0. Import package

   from statistical_iv import api
   

1. Provide a DataFrame as Input:

   • Supply a DataFrame df containing your data for IV calculation.

2. Specify Predictor Variables:

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

3. Define the Target Variable:

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

4. Indicate Variable Types:

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

5. Optional: Set Maximum Bins:

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

6. 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.