Ensemble Learning Framework for Diagnostic and Prognostic Modeling
Apply a Trained Model to New Data
Apply a Trained Prognostic Model to New Data
Train a Bagging Diagnostic Model
Train a Bagging Prognostic Model
Calculate Classification Metrics at a Specific Threshold
Train a Decision Tree Model for Classification
Train an Elastic Net (L1 and L2 Regularized Logistic Regression) Model...
Train an Elastic Net Cox Proportional Hazards Model
Evaluate Diagnostic Model Performance
Evaluate Prognostic Model Performance
Evaluate Predictions from a Data Frame
Evaluate Prognostic Predictions
Plot Diagnostic Model Evaluation Figures
Plot Prognostic Model Evaluation Figures
Generate and Plot SHAP Explanation Figures
Find Optimal Probability Threshold
Train a Gradient Boosting Machine (GBM) Model for Classification
Train a Gradient Boosting Machine (GBM) for Survival Data
Get Registered Diagnostic Models
Get Registered Prognostic Models
Train an EasyEnsemble Model for Imbalanced Classification
Initialize Diagnostic Modeling System
Initialize Prognostic Modeling System
Train a Lasso (L1 Regularized Logistic Regression) Model for Classific...
Train a Lasso Cox Proportional Hazards Model
Train a Linear Discriminant Analysis (LDA) Model for Classification
Load and Prepare Data for Diagnostic Models
Load and Prepare Data for Prognostic Models
Min-Max Normalization
Train a Multi-Layer Perceptron (Neural Network) Model for Classificati...
Run Multiple Diagnostic Models
Run Multiple Prognostic Models
Train a Naive Bayes Model for Classification
Print Diagnostic Model Summary
Print Prognostic Model Summary
Train a Quadratic Discriminant Analysis (QDA) Model for Classification
Register a Diagnostic Model Function
Register a Prognostic Model Function
Train a Random Forest Model for Classification
Train a Ridge (L2 Regularized Logistic Regression) Model for Classific...
Train a Ridge Cox Proportional Hazards Model
Train a Random Survival Forest Model
Train a Stacking Diagnostic Model
Train a Stacking Prognostic Model
Train a Stepwise Cox Proportional Hazards Model
re-export Surv from survival
Train a Support Vector Machine (Linear Kernel) Model for Classificatio...
Train a Voting Ensemble Diagnostic Model
Train an XGBoost Tree Model for Classification
Provides a framework to build and evaluate diagnosis or prognosis models using stacking, voting, and bagging ensemble techniques with various base learners. The package also includes tools for visualization and interpretation of models. The development version of the package is available on 'GitHub' at <https://github.com/xiaojie0519/E2E>. The methods are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655> on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> on stacking.