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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.

Project description

Statistical IV

Optimize your machine learning models with 'Statistical-IV'. Perform automated feature selection based on statistics and customize error control.

  1. Import package

    from statistical_iv import api
    
  2. Provide a DataFrame as Input:

    • Supply a DataFrame df containing your data for IV calculation.
  3. Specify Predictor Variables:

    • Prived a list of predictor variable names (variables_names) to analyze.
  4. Define the Target Variable:

    • Specify the name of the target variable (var_y) in your DataFrame.
  5. Indicate Variable Types:

    • Define the type of your predictor variables as 'categorical' or 'numerical' using the type_vars parameter.
  6. Optional: Set Maximum Bins:

    • Adjust the maximum number of bins for discretization (optional) using the max_bins parameter.
  7. 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:

Output Example

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