Man pages for E2E
Ensemble Learning Framework for Diagnostic and Prognostic Modeling
| apply_dia | Apply a Trained Model to New Data |
| apply_pro | Apply Prognostic Model to New Data |
| bagging_dia | Train a Bagging Diagnostic Model |
| bagging_pro | Train Bagging Ensemble for Prognosis |
| calculate_metrics_at_threshold_dia | Calculate Classification Metrics at a Specific Threshold |
| dt_dia | Train a Decision Tree Model for Classification |
| en_dia | Train an Elastic Net (L1 and L2 Regularized Logistic... |
| en_pro | Train Elastic Net Cox Model |
| evaluate_model_dia | Evaluate Diagnostic Model Performance |
| evaluate_model_pro | Evaluate Prognostic Model Performance |
| evaluate_predictions_dia | Evaluate Predictions from a Data Frame |
| evaluate_predictions_pro | Evaluate External Predictions |
| figure_dia | Plot Diagnostic Model Evaluation Figures |
| figure_pro | Plot Prognostic Model Evaluation Figures |
| figure_shap | Generate and Plot SHAP Explanation Figures |
| find_optimal_threshold_dia | Find Optimal Probability Threshold |
| gbm_dia | Train a Gradient Boosting Machine (GBM) Model for... |
| gbm_pro | Train Gradient Boosting Machine (GBM) for Survival |
| get_registered_models_dia | Get Registered Diagnostic Models |
| get_registered_models_pro | Get Registered Prognostic Models |
| imbalance_dia | Train an EasyEnsemble Model for Imbalanced Classification |
| initialize_modeling_system_dia | Initialize Diagnostic Modeling System |
| initialize_modeling_system_pro | Initialize Prognosis Modeling System |
| int_dia | Comprehensive Diagnostic Modeling Pipeline |
| int_imbalance | Imbalanced Data Diagnostic Modeling Pipeline |
| int_pro | Comprehensive Prognostic Modeling Pipeline |
| lasso_dia | Train a Lasso (L1 Regularized Logistic Regression) Model for... |
| lasso_pro | Train Lasso Cox Proportional Hazards Model |
| lda_dia | Train a Linear Discriminant Analysis (LDA) Model for... |
| load_and_prepare_data_dia | Load and Prepare Data for Diagnostic Models |
| min_max_normalize | Min-Max Normalization |
| mlp_dia | Train a Multi-Layer Perceptron (Neural Network) Model for... |
| models_dia | Run Multiple Diagnostic Models |
| models_pro | Run Multiple Prognostic Models |
| nb_dia | Train a Naive Bayes Model for Classification |
| plot_integrated_results | Visualize Integrated Modeling Results |
| pls_pro | Train Partial Least Squares Cox (PLS-Cox) |
| predict_pro | Generic Prediction Interface for Prognostic Models |
| print_model_summary_dia | Print Diagnostic Model Summary |
| print_model_summary_pro | Print Prognostic Model Summary |
| qda_dia | Train a Quadratic Discriminant Analysis (QDA) Model for... |
| register_model_dia | Register a Diagnostic Model Function |
| register_model_pro | Register a Prognostic Model |
| rf_dia | Train a Random Forest Model for Classification |
| ridge_dia | Train a Ridge (L2 Regularized Logistic Regression) Model for... |
| ridge_pro | Train Ridge Cox Model |
| rsf_pro | Train Random Survival Forest (RSF) |
| stacking_dia | Train a Stacking Diagnostic Model |
| stacking_pro | Train Stacking Ensemble for Prognosis |
| stepcox_pro | Train Stepwise Cox Model (AIC-based) |
| Surv | re-export Surv from survival |
| svm_dia | Train a Support Vector Machine (Linear Kernel) Model for... |
| test_dia | Test Data for Diagnostic Models |
| test_pro | Test Data for Prognostic (Survival) Models |
| train_dia | Training Data for Diagnostic Models |
| train_pro | Training Data for Prognostic (Survival) Models |
| voting_dia | Train a Voting Ensemble Diagnostic Model |
| xb_dia | Train an XGBoost Tree Model for Classification |
| xgb_pro | Train XGBoost Cox Model |
