Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis
Fig 1
Repeated nested cross-validation process for feature selection, hyperparameter selection, and model evaluation.
In each repetition, data is split into 5 folds (1–5). In each fold, 80% are used in an internal CV process for feature selection, hyperparameter optimization, and model training (green squares). The resulting model is tested on the remaining 20% (red square), recording the probability scores and the SHAP values of the random forest model. CV: Cross-validation. SMOTE: Synthetic minority oversampling technique. SHAP: Shapley additive explanations.