R/ShinyHelp/HelpMethod.md

In ChristianAmes/FastRet: Retention time prediction and subset selection with a GUI

Method Selection

Here you can choose by which method the regression model should be trained on. You can choose between Lasso or XGBoost.

Lasso

Lasso (Least absolut shrinkage and selection operator) is based on the Least Minimum Square approach with the extension of a L1 penalty norm. This leads to a selection of variables as well as a generalization of the trained model. Lasso was implemented with the R-package glmnet [2].

XGBoost

XGBoost is a more soffisticated Machine Learning method based on Boosted Regression Trees (BRT) [3]. The main difference to random forest is, that trees are not trained independant from each other but each tree is built with a loss function based on its predecessor. It was implemented with the R-package XGBoost [4].

References

[1] Santosa, Fadil; Symes, William W. (1986). "Linear inversion of band-limited reflection seismograms". SIAM Journal on Scientific and Statistical Computing. SIAM. 7 (4): 1307–1330 [2] Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. [3] Jerome H. Friedman. "Greedy function approximation: A gradient boosting machine.." Ann. Statist. 29 (5) 1189 - 1232, October 2001 [4] Tianqi Chen et. Al, (2021). xgboost: Extreme Gradient Boosting. R package version 1.4.1.1.

ChristianAmes/FastRet documentation built on June 17, 2022, 6 a.m.