From eee37513cab5046422c69f685737516362548338 Mon Sep 17 00:00:00 2001 From: achamma723 Date: Fri, 19 Jul 2024 16:02:26 +0200 Subject: [PATCH] Minor changes --- examples/plot_diabetes_variable_importance_example.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_diabetes_variable_importance_example.py b/examples/plot_diabetes_variable_importance_example.py index 6391ed6..98e2597 100644 --- a/examples/plot_diabetes_variable_importance_example.py +++ b/examples/plot_diabetes_variable_importance_example.py @@ -3,7 +3,7 @@ ======================================= Variable Importance estimates the influence of a given input variable to the -prediction made by a model. To perform variable importance in a prediction +prediction made by a model. To assess variable importance in a prediction problem, :footcite:t:`breimanRandomForests2001` introduced the permutation approach where the values are shuffled for one variable/column at a time. This permutation breaks the relationship between the variable of interest and the @@ -26,7 +26,7 @@ relationship with the outcome. The standard permutation, while breaking the relationship with the outcome, is also destroying the dependency with the remaining variables. Therefore, instead of directly permuting the variable of -interest, the variable of interest is predicted by the mean of the remaining +interest, the variable of interest is predicted by the remaining variables and the residuals of this prediction are permuted before reconstructing the new version of the variable. This solution preserves the dependency with the remaining variables.