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.
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@@ -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.
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@@ -34,7 +38,7 @@ This repository is organized as follows:
*[output](./explainability/output): SHAP and SHAP interaction summary plots.
## Data
The dataset has not been provided since the authors do not have permission for its sharing from the data providers.
