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Parameter Reference Guide

E2E Package Parameter Reference Guide

This guide provides comprehensive parameter documentation for all E2E functions.

Built-in Datasets

E2E includes example datasets for both diagnostic and prognostic modeling:

Diagnostic Datasets

  • train_dia: Training data with sample IDs (column 1), outcomes 0/1 (column 2), and features (columns 3+)
  • test_dia: Test data with the same structure

Prognostic Datasets

  • train_pro: Training data with sample IDs (column 1), survival status 0/1 (column 2), survival time (column 3), and features (columns 4+)
  • test_pro: Test data with the same structure

Built-in Models

Diagnostic Models (12 algorithms)

  • rf: Random Forest
  • xb: XGBoost
  • svm: Support Vector Machine
  • mlp: Multi-Layer Perceptron
  • lasso: L1-regularized Logistic Regression
  • en: Elastic Net
  • ridge: L2-regularized Logistic Regression
  • lda: Linear Discriminant Analysis
  • qda: Quadratic Discriminant Analysis
  • nb: Naive Bayes
  • dt: Decision Tree
  • gbm: Gradient Boosting Machine

Prognostic Models (6 algorithms)

  • lasso_pro: Lasso Cox Regression
  • en_pro: Elastic Net Cox Regression
  • ridge_pro: Ridge Cox Regression
  • stepcox_pro: Stepwise Cox Regression
  • gbm_pro: Gradient Boosting Machine
  • rsf_pro: Random Survival Forest

Diagnostic Modeling Functions

models_dia()

Trains base classification models for diagnostic tasks. Parameters:

bagging_dia()

Bootstrap aggregating ensemble method. Parameters:

voting_dia()

Voting ensemble combining multiple models. Parameters:

stacking_dia()

Stacking ensemble with meta-model. Parameters:

imbalance_dia()

Handles imbalanced datasets using EasyEnsemble-like algorithm. Parameters:

apply_dia()

Applies trained model to new data. Parameters:

evaluate_predictions_dia()

Evaluates model predictions. Parameters:

Prognostic Modeling Functions

models_pro()

Trains base survival models. Parameters:

stacking_pro()

Stacking ensemble for survival analysis. Parameters:

bagging_pro()

Bootstrap aggregating for survival analysis. Parameters:

apply_pro()

Applies trained survival model to new data. Parameters:

evaluate_predictions_pro()

Evaluates survival model predictions. Parameters:

Visualization Functions

figure_dia()

Creates diagnostic model evaluation plots. Parameters:

figure_pro()

Creates prognostic model evaluation plots. Parameters:

figure_shap()

Creates SHAP interpretation plots. Parameters:

Custom Model Registration

register_model_dia() / register_model_pro()

Registers custom algorithms.

Usage: 1. Define custom function following E2E conventions 2. Register with register_model_dia("model_name", custom_function) 3. Use registered model in E2E workflows

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.