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 # Title
 
 ## Introduction
-...
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 ## Dealing with Class Imbalance
 One of the primary challenges we encountered was a significant class imbalance, with a higher number of patients withdrawing from treatment compared to those staying.
@@ -26,6 +26,10 @@ This repository is organized as follows:
     * [cv_metric_gen.py](./model_selection/cv_metric_gen.py): Generating cross-validation metrics and plots for each of the tuned models.
     * [cv_metrics_distr.py](./model_selection/cv_metrics_distr.py): Generating boxplots for each cross-validation metric and tuned model.
     * [test_models.py](./model_selection/test_models.py): Testing tuned models with test dataset.
+    * [output](./model_selection/output):
+        * [hyperparam](./model_selection/output/hyperparam): Excel file containing the optimal hyperparameters for each model in each pipeline.
+        * [cv_metrics](./model_selection/output/cv_metrics): Material related to the results of cross-validation: scores, ROC and Precision-Recall curves and boxplots for each metric. 
+        * [testing](./model_selection/output/testing): Material related to the results of testing the tuned models: scores, ROC and Precision-Recall curves and confusion matrices.
 * [explainability](./explainability):
     * [fit_final_models.py](./explainability/fit_final_models.py): Saving fitted model for each selected final model.
     * [compute_shap_vals.py](./explainability/compute_shap_vals.py): Computing SHAP values for final models.
@@ -34,7 +38,7 @@ This repository is organized as follows:
     * [output](./explainability/output): SHAP and SHAP interaction summary plots.
 
 ## Data
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 The dataset has not been provided since the authors do not have permission for its sharing from the data providers.
+
 For any inquiry you can contact:
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