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I'm using the Gini coefficient to evaluate the performance of a model. Making some changes (feature selection, hyperparameter tuning, etc.) I created variant models with different Gini coefficients.

How can I prove that the improvement in the Gini coefficient is indeed statistically significant?

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In R there is a package called DescTools within it you can find a method for calculating Gini Coefficients, where you can set a confidence level, and get intervals back using a bootstrap method (simulation)

This is how I have seen it done, and probably as meaningful as you need in most situations because you can set alpha to be as conservative as you need.

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  • $\begingroup$ Thanks, I will look into it. Do you know if there is also a python solution? $\endgroup$ Commented Oct 20, 2022 at 14:42

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