Improve Feature Importance Support in Sklearn Khiops Estimators #480

Description

Currently, KhiopsSupervisedEstimator provides the feature_evaluated_importances_ and feature_used_importances_ attributes, which do not match the expected vanilla Scikit-learn estimators' feature_importances_ attribute.

Questions/Ideas

• A solution consists in adding this attribute and set it to an array to the variables' importance attribute for each of the selected variables in the model.
  • Also, the feature_evaluated_importances_ and feature_used_importances_ with the level and weight should be removed, as level and weight are specific to the analysis process, rather than to the model per se.
  • Also, the documentation should be updated to explain that, in v11, the importances are calculated as the average of the exact Shapley values of each feature on the training data.