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.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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