Contents of the package
modeltuner is designed for evaluation of the predictive performance of statistical models and hyperparameter tuning based on cross-validation. The current version of the package supports models with a continuous or binary (0/1) response.
The package also offers some particularly attractive tools for handling what is called iteratively fitted models (IFM) here. For such a model, the fitting process returns not just one single model (or model parameterization), but rather a range of models (or model parameterizations) of increasing structural complexity. Prominent examples (and currently the only instances implemented in the package) are gradient boosting (as implemented in package xgboost) and Lasso regression and elastic nets (available from package glmnet).
Installation
The package can be installed from github by executing
devtools::install_github("MathiasAmbuehl/modeltuner")Dependencies
All package dependencies are available from CRAN. The packages xgboost (>= 1.5), glmnet, ggplot2, MetricsWeighted, matrixStats, RANN, progress are required, while lme4, robustbase, mgcv, rpart, quantreg, ranger, magrittr, tibble, MASS and gridExtra are packages required to reproduced all examples.
Where to start
The best place to start are the two vignettes included in the package:
-
vignette("modeltuner")presents the basic concepts of the package (reading this text first is recommended). -
vignette("ifm")introduces tools suited for developing and evaluating iteratively fitted models (XGBoost and glmnet models).