Robust Tuning and Training for Cross-Source Prediction
Tune and Train External Ridge
Tune and Train External SVM
Tune and Train RobustTuneC Random Forest
Tune and Train RobustTuneC Ridge
Tune and Train External Lasso
Tune and Train External Random Forest
Package Title: Robust Tuning and Training for Cross-Source Prediction
Tune and Train Classifier
Tune and Train Classifier by Tuning Method Ext
Tune and Train External Boosting
Tune and Train by tuning method Int
Tune and Train Internal Boosting
Tune and Train Internal Lasso
Tune and Train Internal Random Forest
Tune and Train Internal Ridge
Tune and Train Internal SVM
Tune and Train Classifier by Tuning Method RobustTuneC
Tune and Train RobustTuneC Boosting
Tune and Train RobustTuneC Lasso
Tune and Train RobustTuneC Support Vector Machine (SVM)
Provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the 'RobustTuneC' method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" <doi:10.1007/s00357-020-09368-z>.